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Record W4379389267 · doi:10.1016/j.jcf.2023.05.014

Potential implicit bias in attribution of adverse events in randomized controlled trials in cystic fibrosis

2023· article· en· W4379389267 on OpenAlexafffund
Ranjani Somayaji, Madeline Wessels, T. Milinic, Kathleen J. Ramos, Nicole Mayer-Hamblett, Bonnie W. Ramsey, Sonya L. Heltshe, Umer Khan, Christopher H. Goss

Bibliographic record

VenueJournal of Cystic Fibrosis · 2023
Typearticle
Languageen
FieldMedicine
TopicCystic Fibrosis Research Advances
Canadian institutionsUniversity of Calgary
FundersNational Institute of Allergy and Infectious DiseasesNational Institute of Diabetes and Digestive and Kidney DiseasesSeattle Children's Research InstituteNational Heart, Lung, and Blood InstituteCanadian Institutes of Health ResearchNational Institutes of HealthCystic Fibrosis Foundation
KeywordsMedicineCystic fibrosisOdds ratioOddsInternal medicineAttributionPlaceboAdverse effectClinical trialDrugRandomized controlled trialLogistic regressionPathologyPsychiatryAlternative medicine

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)0.704
metaresearch head score (Gemma)0.908
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesMetaresearch
DomainCandidate signal: Methods · Consensus signal: Methods
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.296
Threshold uncertainty score0.365

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.7040.908
Meta-epidemiology (narrow)0.0030.005
Meta-epidemiology (broad)0.0110.012
Bibliometrics0.0060.007
Science and technology studies0.0030.012
Scholarly communication0.0100.014
Open science0.0080.008
Research integrity0.0140.014
Insufficient payload (model declined to judge)0.0120.001

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 head0.035
GPT teacher head0.352
Teacher spread0.317 · 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 designObservational
DomainMethods
GenreEmpirical

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

Citations1
Published2023
Admission routes2
Has abstractno

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