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Use and reporting of outcome measures in randomized trials for anti-neutrophil cytoplasmic antibody-associated vasculitis: a systematic literature review

2020· review· en· W3090054636 on OpenAlexaff
Sara Monti, Kaitlin A. Quinn, Robin Christensen, David Jayne, Carol A. Langford, Georgia Lanier, Alfred Mahr, Christian Pagnoux, Maria Bjork Viðarsdóttir, Peter A. Merkel, Gunnar Tómasson

Bibliographic record

VenueSeminars in Arthritis and Rheumatism · 2020
Typereview
Languageen
FieldMedicine
TopicVasculitis and related conditions
Canadian institutionsUniversity of Toronto
FundersEuropean League Against RheumatismAmerican College of Rheumatology Research and Education Foundation
KeywordsMedicineVasculitisRandomized controlled trialAnti-neutrophil cytoplasmic antibodyImmunologyAntibodyOutcome (game theory)Internal medicinePathologyDisease

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)0.060
metaresearch head score (Gemma)0.235
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesnone
DomainCandidate signal: Reporting · Consensus signal: none
Study designCandidate signal: Systematic review · Consensus signal: Systematic review
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.940
Threshold uncertainty score0.318

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0600.235
Meta-epidemiology (narrow)0.0030.002
Meta-epidemiology (broad)0.0220.021
Bibliometrics0.0100.008
Science and technology studies0.0010.002
Scholarly communication0.0060.005
Open science0.0040.004
Research integrity0.0050.004
Insufficient payload (model declined to judge)0.0080.000

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 head0.098
GPT teacher head0.375
Teacher spread0.277 · 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 designSystematic review
DomainReporting
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

Citations23
Published2020
Admission routes1
Has abstractno

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