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Re-analysis of Acute Stroke Trials Using Utility-weighted Modified Rankin Scales (2667)

2020· article· en· W4389450360 on OpenAlexaff
Alexander D. Rebchuk, Zoe R. O’Neill, Elena Szefer, Michael D. Hill, Thalia S. Field

Bibliographic record

VenueNeurology · 2020
Typearticle
Languageen
FieldMedicine
TopicAcute Ischemic Stroke Management
Canadian institutionsUniversity of CalgaryMcGill UniversityUniversity of British Columbia
Fundersnot available
KeywordsModified Rankin ScaleAcute strokeStroke (engine)MedicineClinical trialInternal medicineIschemic strokeTissue plasminogen activator

Abstract

fetched live from OpenAlex

To characterize the between-study variability in utility weighting of the modified Rankin Scale (UW-mRS) in a stroke population and its implications for interpreting the results of clinical trials with a UW-mRS endpoint.

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

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)0.195
metaresearch head score (Gemma)0.315
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesMetaresearch
DomainCandidate signal: Methods · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.805
Threshold uncertainty score0.993

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.1950.315
Meta-epidemiology (narrow)0.0030.001
Meta-epidemiology (broad)0.0070.017
Bibliometrics0.0040.004
Science and technology studies0.0010.001
Scholarly communication0.0050.003
Open science0.0020.002
Research integrity0.0020.003
Insufficient payload (model declined to judge)0.0070.002

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 head0.086
GPT teacher head0.332
Teacher spread0.246 · 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 designSimulation or modeling
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

Citations0
Published2020
Admission routes1
Has abstractyes

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