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4,299,418 works, Canadian by any of four routes.

Every filter state is a URL; the URL is the query; the query is citable via /q/⟨hash⟩. The page, the API and the export parse the same parameters.

The current cohort, streamed from the database: every work column, the machine labels, the provisional scores, and the per-row validation status. Exports are capped at 100,000 rows. Mints a permanent /q/ link for this exact query. The same filters always produce the same link, whoever asks.

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BMJ evidence-based medicine
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Retraction
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Direct Codex and Gemma labels are unvalidated and sparse. Distilled predictions cover the full frame and are also unvalidated. Choose the evidence source explicitly; absence of a direct label is never a negative label.

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The four routes compose: require the funder route and exclude affiliation to get the funder-only stratum no affiliation-based frame ever sees.

125 results · 1 filter active ·
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20182025
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Categories
Machine labels · sparse coverage
Evidence
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An unlabeled work is unknown, not a negative. Label coverage is reported on every query.
125 works in the cohort · of 4,299,418page 1 of 3

Labels cover 115 of 125 works in this cohort. The rest are unlabeled, which is not a negative label: the label table is sparse today and grows as labeling rounds land.

Distilled predictions cover 125 of 125 works in this cohort. Predictions are machine_predicted_unvalidated. The Gemma side is a direct model label for every work (title-only); the Codex side is a distilled, calibrated classifier. Candidate is the union; consensus is the intersection.

aboutno affgemma · no categorygpt · no categorymodels agree
Evidence-based practice education for healthcare professions: an expert view
Elaine Lehane, Patricia Leahy‐Warren, Cliona O’Riordan, Eileen Savage, Jonathan Drennan, Colm O’Tuathaigh +11 more
2018· article· en· BMJ evidence-based medicine· Health Professions
machine prediction:candidate · metaresearchconsensus · none
317
citations
affgemma · metaresearchgpt · no categorymodels split
Reflections on the history of systematic reviews
Mike Clarke, Iain Chalmers
2018· editorial· en· BMJ evidence-based medicine· Decision Sciences
machine prediction:candidate · metaresearchconsensus · metaresearch
81
citations
afffundgemma · metaresearchgpt · metaresearch+research_integritymodels split
Rapid review method series: interim guidance for the reporting of rapid reviews
Adrienne Stevens, Mona Hersi, Chantelle Garritty, Lisa Hartling, Beverley Shea, Lesley Stewart +2 more
2024· article· en· BMJ evidence-based medicine· Decision Sciences
machine prediction:candidate · metaresearchconsensus · metaresearch
55
citations
affgemma · metaresearchgpt · metaresearchmodels agree
Assessing assumptions for statistical analyses in randomised clinical trials
Emil Eik Nielsen, Anders Kehlet Nørskov, Theis Lange, Lehana Thabane, Jørn Wetterslev, Jan Beyersmann +7 more
2019· article· en· BMJ evidence-based medicine· Decision Sciences
machine prediction:candidate · metaresearchconsensus · metaresearch
53
citations
affgemma · no categorygpt · no categorymodels split
Causal inference for clinicians
Steven D. Stovitz, Ian Shrier
2019· article· en· BMJ evidence-based medicine· Mathematics
machine prediction:candidate · noneconsensus · none
34
citations
affgemma · metaresearchgpt · metaresearchmodels agree
Rapid reviews methods series: Guidance on assessing the certainty of evidence
Gerald Gartlehner, Barbara Nußbaumer-Streit, Declan Devane, Leila C. Kahwati, Meera Viswanathan, Valerie King +3 more
2023· article· en· BMJ evidence-based medicine· Decision Sciences
machine prediction:candidate · metaresearchconsensus · metaresearch
30
citations
afffundgemma · metaresearchgpt · metaresearchmodels split
Optimising the process for conducting scoping reviews
Colleen Pawliuk, Helen Brown, Kim Widger, Tammie Dewan, Anne‐Mette Hermansen, Marie-Claude Grégoire +2 more
2020· review· en· BMJ evidence-based medicine· Health Professions
machine prediction:candidate · metaresearchconsensus · metaresearch
28
citations
affgemma · no categorygpt · no categorymodels agree
Problem with patient decision aids
Joshua R Zadro, Adrian C. Traeger, Simon Décary, Mary O’Keeffe
2020· article· en· BMJ evidence-based medicine· Health Professions
machine prediction:candidate · noneconsensus · none
19
citations
affgemma · metaresearchgpt · metaresearchmodels split
Catalogue of bias: novelty bias
Yan Luo, Carl Heneghan, Nav Persaud
2023· article· en· BMJ evidence-based medicine· Computer Science
machine prediction:candidate · metaresearchconsensus · metaresearch
16
citations
affgemma · metaresearchgpt · metaresearchmodels split
Proposed triggers for retiring a living systematic review
M. Hassan Murad, Zhen Wang, Haitao Chu, Lifeng Lin, Ibrahim K El Mikati, Joanne Khabsa +4 more
2023· article· en· BMJ evidence-based medicine· Mathematics
machine prediction:candidate · metaresearchconsensus · metaresearch
15
citations
affgemma · metaresearchgpt · no categorymodels split
Rapid review methods series: Guidance on the use of supportive software
Lisa Affengruber, Barbara Nußbaumer-Streit, Candyce Hamel, Miriam Van der Maten, James Thomas, Chris Mavergames +2 more
2024· article· en· BMJ evidence-based medicine· Decision Sciences
machine prediction:candidate · metaresearchconsensus · metaresearch
15
citations

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