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Commentary: collaborative systematic review may produce and share high-quality, comparative evidence more efficiently

2022· review· en· W4297535932 on OpenAlexafffund
Jill A. Hayden, Rachel Ogilvie, Sareen Singh, Shazia Kashif, Jan Hartvigsen, Christopher G. Maher, Andrea D Furlan, Toby J Lasserson, Peter Tugwell, Maurits W. van Tulder, Amir Qaseem, Manuela L. Ferreira, Rachelle Buchbinder, L. Susan Wieland, Fabianna Resende de Jesus‐Moraleida, Bruno Tirotti Saragiotto, Tiê Parma Yamato, Annemarie de Zoete, Kasper Bülow, Lisandra Almeida de Oliveira, Geronimo Bejarano, Carol Cancelliere

Bibliographic record

VenueJournal of Clinical Epidemiology · 2022
Typereview
Languageen
FieldDecision Sciences
TopicMeta-analysis and systematic reviews
Canadian institutionsDalhousie University
FundersNational Health and Medical Research CouncilMedical Research CouncilCanadian Institutes of Health Research
KeywordsSystematic reviewComparative effectiveness researchEvidence-based medicineQuality (philosophy)Resource (disambiguation)Computer scienceEvidence-based practiceManagement scienceProcess managementMedicineKnowledge managementRisk analysis (engineering)MEDLINEAlternative medicineBusinessPolitical scienceEngineering

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)0.142
metaresearch head score (Gemma)0.589
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesnone
DomainCandidate signal: Methods · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Commentary · Consensus signal: Commentary
Teacher disagreement score0.858
Threshold uncertainty score0.751

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.1420.589
Meta-epidemiology (narrow)0.0030.003
Meta-epidemiology (broad)0.0100.007
Bibliometrics0.0080.009
Science and technology studies0.0040.008
Scholarly communication0.0110.012
Open science0.0110.006
Research integrity0.0480.032
Insufficient payload (model declined to judge)0.0200.010

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 head0.965
GPT teacher head0.750
Teacher spread0.215 · 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 designNot applicable
DomainMethods
GenreCommentary

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

Citations15
Published2022
Admission routes2
Has abstractno

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