MP67-14 SCIENTIFIC AUTHORSHIP OF COCHRANE SYSTEMATIC REVIEWS IN UROLOGY: A 2020 ANALYSIS
Bibliographic record
Abstract
You have accessJournal of UrologyGlobal Health/Humanitarian (MP67)1 Sep 2021MP67-14 SCIENTIFIC AUTHORSHIP OF COCHRANE SYSTEMATIC REVIEWS IN UROLOGY: A 2020 ANALYSIS Ahmad Ozair, Nishanth Subash, and Abhinav Sonkar Ahmad OzairAhmad Ozair More articles by this author , Nishanth SubashNishanth Subash More articles by this author , and Abhinav SonkarAbhinav Sonkar More articles by this author View All Author Informationhttps://doi.org/10.1097/JU.0000000000002028.14AboutPDF ToolsAdd to favoritesDownload CitationsTrack CitationsPermissionsReprints ShareFacebookLinked InTwitterEmail Abstract INTRODUCTION AND OBJECTIVE: Cochrane systematic reviews are widely considered to be amongst the highest level of evidence available with their conclusions often impacting policy and practice. Some countries and/or regions are reported to have a disproportionately higher representation in their authorship. However, it is unclear as to which countries have contributed the highest to Cochrane reviews in urology and what is the current state of collaboration existing between their authors. This study sought to determine authorship patterns and collaborative networks of Cochrane reviews in urology. METHODS: We searched and extracted data from the Cochrane Database for systematic reviews published under the Cochrane Urology Group by 20 June, 2020. We included both active reviews and those withdrawn for updation. For graphical representation, we used the Social Network Visualizer (https://socnetv.org/) and treated a collaborative author group belonging to a single country, e.g., MRC Clinical Trials Unit (UK), as a single author. RESULTS: A total of 80 eligible reviews were found, authored by a total of 430 authors. Co-authors disproportionately belonged to the US (30.5%) and UK (22.3%), followed by Germany (13.5%), South Korea (7.2%), China (4.9%), Australia (4.7%), Brazil (4.4%), Netherlands (3.5%), Canada (1.9%), Argentina and India (1.6% each), Italy (1.6%) and the rest (Fig. 1). A network analysis of collaborations between authors of different countries was generated (Fig. 2). Lead authors belonged maximally to US and UK (23.8% each), followed by South Korea (11.3%) and Germany (10%). Countries/regions, which otherwise contribute actively to urology literature, that had little/no representation included majority of Latin American and African countries, France, Spain and Japan. CONCLUSIONS: The US and the UK are found to be the biggest contributors to the highest-quality evidence in urology, consistent with citation analysis of urology literature. Capacity-building efforts are needed in several countries for getting academic urologists involved with Cochrane. Source of Funding: None © 2021 by American Urological Association Education and Research, Inc.FiguresReferencesRelatedDetails Volume 206Issue Supplement 3September 2021Page: e514-e515 Advertisement Copyright & Permissions© 2021 by American Urological Association Education and Research, Inc.MetricsAuthor Information Ahmad Ozair More articles by this author Nishanth Subash More articles by this author Abhinav Sonkar More articles by this author Expand All Advertisement PDF downloadLoading ...
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.108 | 0.410 |
| Meta-epidemiology (narrow) | 0.001 | 0.003 |
| Meta-epidemiology (broad) | 0.005 | 0.009 |
| Bibliometrics | 0.036 | 0.049 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.011 | 0.010 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.193 | 0.037 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".