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Record W6925270689 · doi:10.17605/osf.io/4me7c

Bibliometric analysis of medical papers that use win ratio in the past decade (2012 to 2024)

2024· other· en· W6925270689 on OpenAlexaboutno aff

Bibliographic record

VenueOpen Science Framework · 2024
Typeother
Languageen
Field
Topic
Canadian institutionsnot available
Fundersnot available
KeywordsBibliometricsBiomedical sciencesBehavioural sciencesMEDLINEHealth scienceHealth services research

Abstract

fetched live from OpenAlex

In this bibliometrics analysis, the focus is on medical sciences papers that have employed the win ratio methodology over the past decade. The win ratio is a relatively novel analytical approach for clinical trials, which uses a composite outcome that emphasizes the relative importance of the different endpoints. It is often used as an alternative to traditional time-to-first-event analysis but is flexible in that multiple types of outcomes can be considered. Project team: Zhenyu Li, University of Ottawa, Ottawa, ON, Canada; Sunnybrook Research Institute, Sunnybrook Health Sciences Centre, Toronto, ON, Canada Aliya Izumi, Sunnybrook Research Institute, Sunnybrook Health Sciences Centre, Toronto, ON, Canada Anika Ranadive, McMaster University, Hamilton, ON, Canada; Sunnybrook Research Institute, Sunnybrook Health Sciences Centre, Toronto, ON, Canada Dr. Dominique Vervoort, Institute of Health Policy, Management and Evaluation, University of Toronto, Toronto, ON, Canada; Sunnybrook Research Institute, Sunnybrook Health Sciences Centre, Toronto, ON, Canada Dr. Stephen Fremes, Division of Cardiac Surgery, Schulich Heart Institute, Sunnybrook Health Sciences Centre, Institute of Health Policy, Management and Evaluation, University of Toronto, Toronto, Canada

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Direct model labels (unvalidated)

Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.

Model armCategoriesStudy designConfidence
gemmaMetaresearchBibliometrics
Domain: Methods · Genre: Other
About the Canadian research system: no · About a Canadian topic: no
Observationalmedium
gptBibliometrics
Domain: not available · Genre: Empirical
About the Canadian research system: no · About a Canadian topic: no
Observationalhigh
models splitAgreement compares identical category sets and study designs across arms.

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.038
metaresearch head score (Gemma)0.236
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Bibliometrics
Consensus categoriesnone
DomainCandidate signal: Methods · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.962
Threshold uncertainty score0.199

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0380.236
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0040.004
Bibliometrics0.2070.268
Science and technology studies0.0020.001
Scholarly communication0.0120.007
Open science0.0020.004
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0140.003

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.

Opus teacher head0.065
GPT teacher head0.399
Teacher spread0.335 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Labeled directly by 2 models reading the full record.

MetaresearchBibliometrics

The models disagree on parts of this classification; every voice is preserved in the section at the end of the page.

Study designObservational
DomainMethods
GenreOther · Empirical

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".

Quick stats

Citations0
Published2024
Admission routes1
Has abstractyes

Explore more

Same venueOpen Science FrameworkCategoryMetaresearchFrench-language works237,207