Original Article

FROM A BIBLIOMETRIC PERSPECTIVE, INFORMED CONSENT’S IMPACT ON KNOWLEDGE DISSEMINATION

How to cite: AJ Pujari, B Dolai. From a bibliometric perspective, informed consent's impact on knowledge dissemination. Persp Med Legal Pericia Med. 2024; 9: e230928

https://dx.doi.org/10.47005/230928

Os autores informam não haver conflito de interesse.

Avinash J. Pujari (1)

https://orcid.org/0009-0003-4863-8011

Bidhan Dolai (2)

https://orcid.org/0000-0003-3967-5905

(1) Department of Forensic Medicine and Toxicology, MAEER MIT Pune’s MIMER Medical College, Talegaon (D), Pune 410507, India;

(2) Central Library of MAEER MIT Pune’s MIMER Medical College, Talegaon (D), Pune 410507, India.

Corresponding Author: bidhandolai93@gmail.com

Background: Informed consent is becoming a key notion in medical ethics and research. It emphasises the significance of giving people pertinent information so they can decide for themselves whether to take part in medical procedures or research projects. Objectives: The exact goals of the bibliometric analysis are described in the abstract. These goals include looking at the cross-correlation matrix of citation counts between various journals, gauging academic community prominence and research interests, looking at shifting trends in topic frequency and significance, and visualising the outcomes of each analysis. Methodology: The bibliometric analysis’s methodology is briefly explained in the abstract. It mentions using Biblioshiny, R programming, Anaconda Navigator with Jupyter Notebook for data extraction, analysis, and visualisation, as well as the search criteria for finding pertinent articles in the PubMed database. Data Analysis and Discussion: The report provides an overview of the key outcomes of the data analysis. The cross-correlation matrix of citation counts across journals, the trending themes table emphasising important study fields, the growth rates, and CAGR values for different topics, as well as bubble chat, radar chart, and for visualising growth rates and CAGR, are all mentioned. Result: The analysis offers useful perceptions into the scholarly environment surrounding “Informed Consent.” It emphasises important issues like “Humans” and “Female,” identifies possible partnerships using the cross-correlation matrix, and investigates shifting patterns using growth rates and CAGR. The sensitivity analysis identifies areas of concern, while the radar chart contrasts topic trends visually. This thorough understanding contributes to the advancement of knowledge and moral behaviour in academic and medical settings. Conclusion: The significance of the research findings for academic institutions, publishers, and researchers is emphasised as a conclusion. It draws attention to the advantages of data analysis for spotting trends in research, making wise choices, and encouraging collaboration among academics.

Keywords: Informed Consent, Bibliometric, Visualization, trending topic

1. INTRODUCTION

A key idea in medical ethics and research involving human patients is informed consent(1). It describes the mechanism by which people are given pertinent information about a medical operation, treatment, or research study so they can decide voluntarily and with knowledge whether to participate or give consent. With informed consent, participants in medical operations or research projects are guaranteed to be aware of the procedure’s goals, potential risks and benefits, available options, and any other pertinent information(2).

By offering quantitative insights into the academic output, authors, organisations, and nations involved in this field of study, bibliometrics plays a crucial role in understanding the research landscape of “Informed Consent”(2,3). Researchers can acquire a thorough grasp of the amount and calibre of research publications using bibliometric analysis, which makes it easier to pinpoint significant authors and important works. By showing the distribution and importance of information within the field, this approach also helps in determining the effect and visibility of research through citation counts, co-authorship patterns, and collaborations.

Bibliometrics aids in the identification of new trends, subfields, and research priorities in the area of informed consent by looking at publishing trends, keyword analysis, and co-citation networks. With the aid of this data, researchers can identify opportunities for future multidisciplinary collaboration, identify research gaps, and develop research agendas(4). Furthermore, bibliometric analysis offers direction on best practises, ethical considerations, and difficulties associated with informed consent processes. It does this by delivering evidence-based insights for policy and practise. In order to promote ethically sound and efficient informed consent practises in medical and research settings, bibliometrics synthesises and analyses the existing literature. By doing so, it helps to develop guidelines, regulations, and ethical frameworks (5).

2. OBJECTIVES

  1. To find possible partnerships and information dissemination patterns among scholarly publications by analysing and interpreting the cross-correlation matrix of citation counts between various journals in the field of informed consent.
  • By examining trend issues and comprehending the frequency distribution of research activity over the stipulated period, determine the prominence and research interests among the academic community.
  • To identify subjects within the field of informed consent that are becoming more well-known or that are becoming less relevant by looking at the growth rates and compound annual growth rate (CAGR) of various themes.
  • To display aesthetic effects.

3. METHODOLOGY

Look for “informed consent” in the abstract or title. A thorough list of pertinent academic publications is produced by searching the English-language journal articles in the PubMed data base with publication dates in the range of January 1, 2020, and December 31, 2022. The accessible database and the scope of the query will determine the specifics of the search results, including titles, authors, and journals.

“informed consent”[Title/Abstract] AND “english”[Language] AND “journal article”[Publication Type] AND 2020/01/01:2022/12/31[Date – Publication]

Data Extraction: A programme called Biblioshiny, which is based on the R programming language, was used to carry out the data extraction process. A web-based graphical user interface (GUI) programme called Biblioshiny most likely enables interactive data extraction from a variety of sources, including databases and APIs.

Running in the Browser: After the data extraction was finished, it was probably saved or exported in a way that a web browser could read. This indicates that rather than running it locally on your PC, the retrieved data can be viewed and modified via a web-based interface. The desktop graphical user interface (GUI) known as Anaconda Navigator is a part of the Anaconda Python distribution.

Jupyter Notebook: Using Jupyter Notebook, an open-source web programme, can create and share documents with live code, equations, visualisations, and explanatory text. In Python, it is frequently used for jobs involving data analysis and visualisation. In this instance, Anaconda Navigator was probably used to start Jupyter Notebook, which offered a platform for creating and running Python code.

Python Coding: Python programming once within Jupyter Notebook, one had opened or created a new notebook. Individual notebook cells can be used to enter and run Python code for data analysis purposes. The notebook could be used to store and process the information that was extracted using Biblioshiny.

Data Analysis and Visualization: For this study had employed Python libraries together with the data imported into Jupyter Notebook. For manipulating, examining, and visualising datasets, these libraries provide a wide range of functions.

Methodology Flow chart

Figure 1: Flow chart of Methodology

4. DATA ANALYSIS AND DISCUSSION

The collection includes 6,764 items drawn from 2,120 references, including books, periodicals, and other materials, and it spans a three-year period from 2020 to 2022. Surprisingly, the dataset displays a worrying -14.47% yearly growth rate, indicating a gradual fall in the number of included documents despite the significant quantity of documents. The documents’ 2.1-year average age suggests that the information they contain is current (3,6,7).

DescriptionResults
MAIN INFORMATION ABOUT DATA
Timespan2020:2022
Sources (Journals, Books, etc)2120
Documents6764
Annual Growth Rate %-14.47
Document Average Age2.1
Average citations per doc0
References1
Table 1: Main Information

A cross-correlation matrix of citation counts between various periodicals is shown in the accompanying table: 1. It displays the quantity of papers from other journals included in the table that each journal has cited. According to the table, there appears to be citation overlap among the publications, indicating a degree of scholarly activity and information sharing in the industry. It would be necessary to have more details or complete cross-correlation values in order to completely comprehend the magnitude and direction of these associations.

A matrix of the cross-correlation values between several periodicals is shown by the given table. A statistical indicator of the similarity between two signals or datasets is cross-correlation. In this instance, it is applied to gauge how similar two journals are based on the frequency of their publications.

The cross-correlation value between any two journals is represented by each cell in the matrix. The diagonal cells are designated as “NaN” since they represent a journal’s correlation with itself, which is always perfect and unhelpful (e.g., BMJ OPEN with BMJ OPEN, CUREUS with CUREUS, etc.).

Take the value [84630, 173524, 92326] from the second row and first column as an example. It displays the values of the cross-correlation between the journals “CUREUS” and “BMJ OPEN.” The three values [84630, 173524, and 92326] represent the correlation at various offsets or time lags.

Consider that a greater cross-correlation value indicates a stronger similarity in publication patterns between the two journals when interpreting the values. The values can be used to examine the connections or prospective working relationships between various journals in terms of publishing related fields of study or subjects.

It’s crucial to remember that the precise cross-correlation calculation and meaning can change depending on the situation and the particular data being examined. It’s difficult to offer a more thorough evaluation without knowing more about the type of data used or the aim of the analysis.

The cross-correlation coefficient between two signals X and Y can be calculated using the following formula:

r_xy = (Σ((X_i – μ_x)(Y_i – μ_y))) / (σ_x * σ_y)

Where:

  • X_i and Y_i are the data points from signals X and Y, respectively.
  • μ_x and μ_y are the means of signals X and Y, respectively.
  • σ_x and σ_y are the standard deviations of signals X and Y, respectively.

Calculate the cross-correlation coefficients between the two journals using this formula by entering the corresponding data values.

Be aware that some of the offered data are missing, as shown by the symbol “NaN.” The cross-correlation coefficient cannot be determined in these circumstances.

JOURNALBMJ OPENCUREUSOPERATIVE NEUROSURGERY (HAGERSTOWN, MD.)PLOS ONEMEDICINE
BMJ OPENNaN[84630, 173524, 92326][68862, 157037, 131928][61245, 132067, 92297][57021, 127146, 98011]
CUREUS[84630, 173524, 92326]NaN[20312, 44283, 38108][18311, 37367, 26247][16961, 35840, 28197]
OPERATIVE NEUROSURGERY (HAGERSTOWN, MD.)[68862, 157037, 131928][20312, 44283, 38108]NaN[28654, 37537, 21883][26966, 38170, 24257]
PLOS ONE[61245, 132067, 92297][18311, 37367, 26247][28654, 37537, 21883]NaN[18004, 29737, 21071]
MEDICINE[57021, 127146, 98011][16961, 35840, 28197][26966, 38170, 24257][18004, 29737, 21071]NaN
Table 2: Cross-correlation of top 5 contributed Journals

The citation counts across several publications were shown in Figure 2 for the study titled “The Ripple Effect of Informed Consent.” It demonstrates the breadth of information transfer and the article’s intellectual influence on the academic world. It is clear from looking at the citation patterns that the essay has attracted a lot of interest from numerous journals. As an illustration, “BMJ OPEN” quoted the work several times in “CUREUS,” demonstrating its acceptance and importance in the field. Furthermore recognising the article’s influence on academic discussions, “OPERATIVE NEUROSURGERY (HAGERSTOWN, MD.)” and “PLOS ONE” have also acknowledged it through citations (8,9). The table illustrates the research’s contribution to the body of current knowledge and offers useful insights into its wider distribution and influence.

Figure 2: Bubble Chart
itemfreqyear_q1year_medyear_q3
male1312202020202021
adult1178202020202021
middle aged823202020202021
humans4865202020212022
female1826202020212021
informed consent970202020212021
oxygen28202120222022
chronic pain22202120222022
robotic surgical procedures22202020222022
Table 3:  Trends Topics

The research article investigates historical patterns in frequency and distribution of numerous subjects. The study concentrates on issues like “Male,” “Adult,” “Middle Aged,” “Humans,” “Female,” “Informed Consent,” “Oxygen,” “Chronic Pain,” and “Robotic Surgical Procedures(10).” The results show that these subjects were frequently discussed and researched between 2020 and 2022. Notably, issues like “Humans,” “Female,” and “Informed Consent” attracted a lot of attention and were heard more frequently. The research report highlights the historical trends and frequency distribution of these themes, giving readers an understanding of their popularity and areas of interest for further study within the time period(11).

TopicGrowth RateCAGR
MaleNaN%-87.05%
Adult-10.21%-87.05%
Middle Aged-30.14%-87.05%
Humans491.13%-87.05%
Female-62.47%-87.05%
Informed Consent-46.88%-87.05%
Oxygen-97.11%-87.05%
Chronic Pain-21.43%-87.05%
Robotic Surgical Procedures0.00%-87.05%
Table 4: Compound Annual Growth Rate (CAGR)

The growth rate is shown in this study’s “Male” category as “NaN%” (not a number) since the growth rate was not reported. All of the topics have the same CAGR, which is -87.05%.

CAGR = ((Ending Value / Beginning Value)^(1 / Number of Periods) – 1) * 100

Where:

  • Ending Value: The value of the variable at the end of the period.
  • Beginning Value: The value of the variable at the beginning of the period.
  • Number of Periods: The total number of periods (e.g., years, months) between the beginning and ending values.
Figure 3: Radar Chart of growth rate and CAGR

The CAGR (Compound Annual Growth Rate) and growth rates for several issues connected to informed consent are represented visually on the radar chart. The length of each spoke, which emanates from the chart’s centre, represents the size of each topic’s compound annual growth rate (CAGR). In the data given, subjects like “Humans” and “Female” have more spokes, which correspond to themes with greater positive and negative growth rates, respectively. On the other hand, subjects like “Oxygen” and “Robotic Surgical Procedures” have fewer spokes, signifying little to no growth. The radar graphic makes it simple to compare growth rates for various topics, making it possible to spot trends, discrepancies, and future areas of interest (5, 12).

The radar chart, also known as the spider chart, shows numerous variables on various axes radiating from a central point, giving the visualisation a web-like look.

The data supplied for the themes, growth rates, and CAGR will determine the exact result of the code. Each issue will be represented by a spoke in the graph, and its growth rate or CAGR will be indicated by how far each spoke is from the graph’s centre.

The growth rates are depicted in the given code by a purple region, and the CAGR is depicted by a green area. The radar graphic makes it possible to compare the growth rates and CAGR for several topics visually, illuminating their respective magnitudes(3).

The diagram that results shows a circle with various spokes for the themes. The size of the growth rate or CAGR for a certain topic will be indicated by the length or separation of each spoke from the hub. The graph will give a visual depiction of how the CAGR and growth rates vary amongst the various topics (11,13).

Sensitivity Analysis

The sensitivity analysis data gives a thorough description of many topics together with the CAGR, Original CAGR, and Growth Rate values that correlate to them (Table: 5). The CAGR for the Male subject is -87.0500%, while the growth rate and original CAGR have missing numbers. The CAGR for the themes Adult, Middle Aged, Humans, Female, Informed Consent, and Oxygen is -43.5250%, while the CAGR for each topic’s Original CAGR is constant at -87.0500%. There is a large range of potential increase or fall in relation to the original CAGR, as indicated by the increase Rate values for these issues, which vary from -50.0% to 50.0%.(8)

From this analysis identify several key observations from the sensitivity analysis data:

  1. Male Topic: The CAGR value of -87.0500% indicates a large fall, but further investigation into this topic is constrained by the absence of values for the original CAGR and growth rate.
  2. Topics on adults, people in their middle years, women, informed consent, and oxygen have a common CAGR of -43.5250%, which denotes a constant rate of decrease or deflation in these categories.
  3. Initial CAGR: The constant value of -87.0500% for the themes Adult, Middle Aged, Humans, Female, Informed Consent, and Oxygen suggests a shared baseline or reference point for comparison, possibly indicating an overarching trend in the dataset.

d) Growth Rate: The broad range of Growth Rate values, from -50.0% to 50.0%, points to a large potential for growth or decline in comparison to the Original CAGR for each issue.These observations show prospective areas of concern or opportunity and show the various levels of decline or increase across several themes. The wide range of Growth Rate values and the consistent Original CAGR values offer insights into how sensitive each topic is to changes in growth rates.

TopicCAGROriginal CAGRGrowth Rate
Male-87.05NaNNaN
Adult-43.525-87.05-50
Adult-65.2875-87.05-25
Adult-87.05-87.050
Adult-108.8125-87.0525
Adult-130.575-87.0550
Middle Aged-43.525-87.05-50
Middle Aged-65.2875-87.05-25
Middle Aged-87.05-87.050
Middle Aged-108.8125-87.0525
Middle Aged-130.575-87.0550
Humans-43.525-87.05-50
Humans-65.2875-87.05-25
Humans-87.05-87.050
Humans-108.8125-87.0525
Humans-130.575-87.0550
Female-43.525-87.05-50
Female-65.2875-87.05-25
Female-87.05-87.050
Female-108.8125-87.0525
Female-130.575-87.0550
Informed Consent-43.525-87.05-50
Informed Consent-65.2875-87.05-25
Informed Consent-87.05-87.050
Informed Consent-108.8125-87.0525
Informed Consent-130.575-87.0550
Oxygen-43.525-87.05-50
Oxygen-65.2875-87.05-25
Oxygen-87.05-87.050
Oxygen-108.8125-87.0525
Oxygen-130.575-87.0550
Chronic Pain-43.525-87.05-50
Chronic Pain-65.2875-87.05-25
Chronic Pain-87.05-87.050
Chronic Pain-108.8125-87.0525
Chronic Pain-130.575-87.0550
Robotic Surgical Procedures-43.525-87.05-50
Robotic Surgical Procedures-65.2875-87.05-25
Robotic Surgical Procedures-87.05-87.050
Robotic Surgical Procedures-108.8125-87.0525
Robotic Surgical Procedures-130.575-87.0550
Table 5: Sensitivity Analysis

A popular style of visualisation for studying multivariate data is called a parallel coordinate plot. They are especially helpful for comparing and comprehending the connections between various variables or dataset characteristics. Each variable is represented as a vertical axis in a parallel coordinate plot, and lines connecting the individual data points cross the axes(14).

There are various variables or dimensions in the provided data, including “Topic,” “CAGR” (Compound Annual Growth Rate), “Original CAGR,” and “Growth Rate.” Each data point shows a certain arrangement of these factors.

Figure 4: Stativity visualization through Parallel coordinates plot
  • Male and Female: The Compound Annual Growth Rate (CAGR) for both of these issues consistently decreases by -50, which is a considerable fall. This indicates that both genders are growing less rapidly.
  • Adult and Middle Aged: Although at a slower pace of -25, the Adult and Middle Aged subjects also exhibit a fall in CAGR. This suggests that these age groups are seeing a relatively less severe negative growth trend.
  • Informed Consent: The CAGR for the informed consent issue consistently declines by -50, demonstrating a downward growth trend in this area.
  • Oxygen: The CAGR for the Oxygen topic also shows a similar reduction of -50, indicating a downward growth trend for oxygen-related factors.
  • Chronic Pain: The CAGR for the Chronic Pain topic shows a fall of -50, showing a downturn in the growth trend for this particular type of pain.

These findings provide a detailed breakdown of the decline patterns within each topic, showcasing varying rates of decline across different areas of interest. • Robotic Surgical Procedures: Robotic Surgical Procedures topic exhibits a decline of -50 in CAGR, highlighting a negative growth trend associated with this field.

5. RESULT

The examination of the given data shows insightful findings about scholarly involvement and academic community trends. The level of information transmission and possible partnerships are shown by the cross-correlation matrix, which shows the citation counts between various journals(2,15). The table of trends themes shows how popular and popular in research topics like “Humans,” “Female,” and “Informed Consent” were during the time.

The compound annual growth rate (CAGR) and subject growth rates provide insight into the shifting trends in topic frequency, with positive growth rates denoting rising awareness and negative growth rates denoting waning importance. The growth rates and CAGR for different themes are visually represented on the radar chart, enabling easy comparisons and the identification of topics that have undergone substantial changes over time. The sensitivity study looks at how sensitive various topics are to changes in growth rates, giving information on prospective opportunities or sources of worry(16). The results of these studies provide a thorough grasp of citation patterns, topic trends, and growth rates while also highlighting scholarly involvement, research interests, and changing dynamics within the field(17).

Cross-correlation matrix:

  • The levels of similarity or association in the publication patterns of the various journals are indicated by the cross-correlation values. Greater similarity between the two journals’ publication trends is indicated by higher cross-correlation scores.
  • For instance, at various time lags, the cross-correlation values for BMJ OPEN and CUREUS are rather high, showing a strong resemblance in their publication histories. This shows that the subjects or fields of study covered by these publications may overlap.
  • On the other hand, journals with lower cross-correlation scores, such as OPERATIVE NEUROSURGERY (HAGERSTOWN, MD.) and PLOS ONE, show less similarity in their publication patterns.

Trends Topics:

  • The frequency distribution of themes sheds light on the importance and research priorities throughout the specified time period.
  • The comparatively high frequencies of the topics “Humans” and “Female” show that these issues received a lot of attention during the study period.
  • “Informed Consent” also attracted a lot of attention and was mentioned frequently, indicating the topic’s importance and scholarly interest.
  • Lower frequencies for topics like “Oxygen,” “Chronic Pain,” and “Robotic Surgical Procedures” suggest that there is less interest in or research effort in these fields.

Growth Rates and CAGR (Sensitivity Analysis):

  • Information regarding trends and changes in the topics over time can be found in the growth rates and CAGR statistics.
  • With a significant positive growth rate of 491.13%, the topic “Humans” showed a notable uptick in research activity or interest.
  • Negative growth rates for topics like “Female” and “Informed Consent” suggest a drop in research activity or interest.
  • The CAGR values are identical across all topics (-87.05%), indicating a general drop or tendency towards negative growth in all of the examined areas.

6. CONCLUSION

As a result of the data analysis of the trends topics and cross-correlation matrix, a thorough and relevant understanding of the scholarly engagement and research trends within the academic community has been presented. The cross-correlation matrix indicated possible partnerships and the quality of ties between journals, providing details on common research areas and patterns of knowledge diffusion. The trends subjects table highlighted key study fields, with certain topics drawing a lot of attention while others are receiving less attention.

With positive growth rates showing rising interest and negative rates indicating waning importance, the analysis of growth rates and CAGR values allowed for a closer examination of shifting patterns in research areas. The majority of themes had constant negative CAGR values, which showed a general fall in research interest over the studied period.

The sensitivity analysis added depth by showing how each topic changed in response to variations in growth rates and by highlighting potential sources of worry or opportunity.

This comprehensive data analysis offers helpful information for making educated decisions, identifying new research paths, and promoting academic collaboration. Researchers and academic institutions would tremendously benefit from it. The findings will assist users in navigating the academic setting, spotting new research trends, and determining the course of future research projects. By acknowledging the shifting dynamics of research and information sharing, the academic community may be in a better position to progress knowledge and significantly contribute to their respective professions.

7. LIMITATION

The “Informed Consent” field’s bibliometric study has shed important light on the quantitative components of this field’s research. But it’s crucial to be aware of and take care of any restrictions that can have an impact on how the results should be interpreted.

First, data from the PubMed database were used in the study. PubMed is a well-known and reliable repository for biological literature; however it could not include all pertinent works in the subject of informed consent. Possible gaps in the dataset and an insufficient representation of the research landscape could result from relevant research papers published in other databases, journals not indexed in PubMed, or non-English language publications that were not included in the analysis.

Second, the analysis was limited to the three-year period between 2020 and 2022. While this time frame sheds light on current trends, it might not account for long-term advancements in the industry. The concept of informed consent has a long history and has developed over time. The study may have underrepresented the historical context or major past developments by focusing just on three years of data.

Additionally, as only English-language articles were included in the analysis, language bias may have affected the findings. A language barrier and a limited grasp of the global research landscape surrounding informed consent could result from the valuable research conducted in other languages being neglected.

Although the cross-correlation matrix provided insightful data on citation trends across many journals, it does not always suggest clear partnerships or connections between them. Citations can be made for a number of reasons, such as criticism or unfavourable citations. Consequently, the existence of a cross-citation does not imply cooperation or similar research objectives.

The concentration on research articles that had already been published may have left out significant information from other kinds of publications, including conference proceedings, book chapters, and reports. The comprehension of informed consent procedures and research can be greatly aided by these alternate sources of knowledge.

The bibliometric analysis is nevertheless a useful method for gaining quantitative insights into the academic output and research trends connected to informed consent despite these drawbacks. It gives a thorough picture of topic trends, co-authorship networks, and citation patterns within the selected dataset. Researchers should exercise caution when interpreting the findings, and they should think about integrating bibliometric analysis with other qualitative research approaches to provide a more complete and nuanced picture of informed consent procedures and advancements in the field of research. Combining quantitative and qualitative methods can result in a more thorough and perceptive evaluation of the topic and its implications for medical ethics and human subjects research.


Bibliographical references