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Record W4376130123 · doi:10.1016/j.cmi.2023.05.003

Unlocking the DOOR—how to design, apply, analyse, and interpret desirability of outcome ranking endpoints in infectious diseases clinical trials

2023· review· en· W4376130123 on OpenAlexaff
Sean Wei Xiang Ong, Neta Petersiel, Mark Loewenthal, Nick Daneman, Steven Y. C. Tong, Joshua S. Davis

Bibliographic record

VenueClinical Microbiology and Infection · 2023
Typereview
Languageen
FieldDecision Sciences
TopicMeta-analysis and systematic reviews
Canadian institutionsHealth Sciences CentreUniversity of TorontoSunnybrook Health Science Centre
Fundersnot available
KeywordsRanking (information retrieval)Clinical trialMedicineMEDLINERandomized controlled trialMetric (unit)Outcome (game theory)Clinical endpointClinical study designIntensive care medicinePost hocComputer scienceArtificial intelligenceSurgeryInternal medicineBusinessMarketingPolitical scienceEconomics

Abstract

fetched live from OpenAlex
No abstract in any covered source. Its absence is recorded, not treated as a negative.

No abstract. This is not a gap in this database; OpenAlex has none either. 23.3% of the frame is in this state, and the screen finds HALF as much metaresearch here, so the absence is a measured bias rather than a missing field.

How this classification was reachedexpand

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)0.697
metaresearch head score (Gemma)0.878
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesMetaresearch
DomainCandidate signal: Methods · Consensus signal: Methods
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Methods · Consensus signal: Methods
Teacher disagreement score0.303
Threshold uncertainty score0.373

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.6970.878
Meta-epidemiology (narrow)0.0070.008
Meta-epidemiology (broad)0.0390.035
Bibliometrics0.0130.010
Science and technology studies0.0030.012
Scholarly communication0.0220.025
Open science0.0100.012
Research integrity0.0110.023
Insufficient payload (model declined to judge)0.0080.002

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 head0.903
GPT teacher head0.666
Teacher spread0.237 · 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

Machine predicted; the direct Gemma label and the distilled Codex classifier agree on what is shown here.

Study designTheoretical or conceptual
DomainMethods
GenreMethods

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

Citations41
Published2023
Admission routes1
Has abstractno

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