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Record W4312211303 · doi:10.1016/j.jvs.2022.12.029

Assessing the robustness of negative vascular surgery randomized controlled trials using their reverse fragility index

2022· review· en· W4312211303 on OpenAlexaff
Allen Li, Arshia P. Javidan, Eva Liu, Aryan Ahmadvand, Derrick Y. Tam, Faysal Naji, Thomas L. Forbes

Bibliographic record

VenueJournal of Vascular Surgery · 2022
Typereview
Languageen
FieldMedicine
TopicCardiac, Anesthesia and Surgical Outcomes
Canadian institutionsMcMaster UniversityUniversity of SaskatchewanToronto Rehabilitation InstituteUniversity Health NetworkUniversity of TorontoOttawa HospitalUniversity of Ottawa
Fundersnot available
KeywordsMedicineInterquartile rangeRandomized controlled trialConfidence intervalSurgeryVascular surgeryInternal 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)0.174
metaresearch head score (Gemma)0.454
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesnone
DomainCandidate signal: Methods · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.826
Threshold uncertainty score0.922

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.1740.454
Meta-epidemiology (narrow)0.0030.001
Meta-epidemiology (broad)0.0150.024
Bibliometrics0.0050.004
Science and technology studies0.0010.004
Scholarly communication0.0060.005
Open science0.0040.003
Research integrity0.0050.006
Insufficient payload (model declined to judge)0.0110.001

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 head0.141
GPT teacher head0.379
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; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

Study designObservational
DomainMethods
GenreReview

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

Citations12
Published2022
Admission routes1
Has abstractno

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