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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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Healthcare Policy and Management
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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.

affaffiliation
fundfunder
venuejournal
aboutaboutness

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

3,600 results · 1 filter active ·
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20002025
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Machine labels · sparse coverage
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An unlabeled work is unknown, not a negative. Label coverage is reported on every query.
3,600 works in the cohort · of 4,299,418page 40 of 72

Labels cover 5 of 3,600 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 3,600 of 3,600 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.

affno abstractunlabeled
Planning for Retirement
Bill Nelems
2011· article· en· Thoracic surgery clinics/Thorac. surg. clin.· Economics, Econometrics and Finance
machine prediction:candidate · noneconsensus · none
1
citations
affunlabeled
Patient access: How do we measure it?
Jodi Polaha, Nadiya Sunderji
2019· editorial· en· Families Systems & Health· Economics, Econometrics and Finance
machine prediction:candidate · noneconsensus · none
1
citations
aboutno affunlabeled
How to Choose?
Robert Chernomas, Ardeshir Sepehri
2018· book· en· Economics, Econometrics and Finance
machine prediction:candidate · noneconsensus · none
1
citations
venueaboutno affunlabeled
Canadian physician supply on uptick
Adam Miller
2012· article· en· Canadian Medical Association Journal· Economics, Econometrics and Finance
machine prediction:candidate · noneconsensus · none
1
citations
venueaboutno affunlabeled
Health Canada wants more funds from pharma
Lauren Vogel
2017· article· en· Canadian Medical Association Journal· Economics, Econometrics and Finance
machine prediction:candidate · noneconsensus · none
1
citations
affno abstractunlabeled
The Era of Big Performance Measurement: Here at Last?
Peter K. Lindenauer, Kaveh G Shojania
2008· editorial· en· The Joint Commission Journal on Quality and Patient Safety· Economics, Econometrics and Finance
machine prediction:candidate · noneconsensus · none
1
citations
aboutno affunlabeled
Physician population stabilizes.
Lynda Buske
2004· article· en· Europe PMC (PubMed Central)· Economics, Econometrics and Finance
machine prediction:candidate · noneconsensus · none
1
citations
affunlabeled
Examining Safer Opioid Supply Policies
Hudson Reddon, Paxton Bach, M‐J Milloy
2024· article· en· JAMA Internal Medicine· Economics, Econometrics and Finance
machine prediction:candidate · noneconsensus · none
1
citations
affunlabeled
Administrative Databases
Leslíe L. Roos, Patrick S. Romano, Patricia Fergusson
2005· other· en· Encyclopedia of Biostatistics· Economics, Econometrics and Finance
machine prediction:candidate · noneconsensus · none
1
citations
affno abstractunlabeled
Medicaid‑Insured Children with Medical Complexity in a Rural State
James C. Bohnhoff, Chelsea E. F. Bodnar, Jon Graham, Jonathon Knudson, Cindy S. Leary, Lauren Cater +1 more
2024· article· en· Academic Pediatrics· Economics, Econometrics and Finance
machine prediction:candidate · noneconsensus · none
1
citations
aboutno affunlabeled
'Lucky' seven times four.
David B. Nash
2011· article· en· PubMed· Economics, Econometrics and Finance
machine prediction:candidate · noneconsensus · none
1
citations
affvenueaboutunlabeled
Using Data to Move from Volume to Value
Jason M. Sutherland
2019· article· en· A Nudge Too Far? A Nudge at All? On Paying People to Be Healthy· Economics, Econometrics and Finance
machine prediction:candidate · noneconsensus · none
1
citations
affvenueaboutunlabeled
The Tragedy of the Medicare Commons?
Peter Barrett
2001· letter· en· A Nudge Too Far? A Nudge at All? On Paying People to Be Healthy· Economics, Econometrics and Finance
machine prediction:candidate · noneconsensus · none
1
citations
affno abstractunlabeled
Rebuttal From Dr Rubenfeld
Gordon D. Rubenfeld
2016· letter· en· CHEST Journal· Economics, Econometrics and Finance
machine prediction:candidate · noneconsensus · none
1
citations
affvenueaboutunlabeled
Health System Leadership and the Federal Role in Canada
Gregory P. Marchildon
2014· letter· en· A Nudge Too Far? A Nudge at All? On Paying People to Be Healthy· Economics, Econometrics and Finance
machine prediction:candidate · noneconsensus · none
1
citations

How this was built: Screen · Findings · About