MP67-14 SCIENTIFIC AUTHORSHIP OF COCHRANE SYSTEMATIC REVIEWS IN UROLOGY: A 2020 ANALYSIS
Notice bibliographique
Résumé
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 We included both active reviews and those withdrawn for updation.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 ...
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,108 | 0,410 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,003 |
| Méta-épidémiologie (sens large) | 0,005 | 0,009 |
| Bibliométrie | 0,036 | 0,049 |
| Études des sciences et des technologies | 0,002 | 0,001 |
| Communication savante | 0,011 | 0,010 |
| Science ouverte | 0,002 | 0,009 |
| Intégrité de la recherche | 0,003 | 0,004 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,193 | 0,037 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».