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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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Asthma and respiratory diseases
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.

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.

5,532 results · 1 filter active ·
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20002025
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Machine labels · sparse coverage
Evidence
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An unlabeled work is unknown, not a negative. Label coverage is reported on every query.
5,532 works in the cohort · of 4,299,418page 9 of 111

Labels cover 27 of 5,532 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 5,532 of 5,532 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.

venueno affunlabeled
Telehealthcare for asthma: a Cochrane review
Susannah McLean, David Chandler, Ulugbek Nurmatov, Joseph N. Liu, Claudia Pagliari, Josip Car +1 more
2011· review· en· Canadian Medical Association Journal· Medicine
machine prediction:candidate · noneconsensus · none
129
citations
afffundno abstractunlabeled
Sputum autoantibodies in patients with severe eosinophilic asthma
Manali Mukherjee, David Bulir, Katherine Radford, Melanie Kjarsgaard, Chynna Huang, Elizabeth A. Jacobsen +7 more
2017· article· en· Journal of Allergy and Clinical Immunology· Medicine
machine prediction:candidate · noneconsensus · none
129
citations
affno abstractunlabeled
Trends in the Prevalence of Asthma
Malcolm R. Sears
2014· article· en· CHEST Journal· Medicine
machine prediction:candidate · noneconsensus · none
128
citations
afffundunlabeled
Fatty airways: implications for obstructive disease
John Elliot, Graham M. Donovan, Kimberley C. W. Wang, Francis H. Y. Green, Alan James, Peter B. Noble
2019· article· en· European Respiratory Journal· Medicine
machine prediction:candidate · noneconsensus · none
127
citations
affunlabeled
Small airway inflammation in asthma
Meri K. Tulić, Pota Christodoulopoulos, Qutayba Hamid
2001· review· en· Respiratory Research· Medicine
machine prediction:candidate · noneconsensus · none
126
citations
affunlabeled
Asthma: prevalence and cost of illness
Stephanie Stock, Marcus Redaèlli, Markus Luengen, G Wendland, Daniele Civello, Karl W. Lauterbach
2005· article· en· European Respiratory Journal· Medicine
machine prediction:candidate · noneconsensus · none
125
citations
afffundunlabeled
The revised 2014 GINA strategy report
Louis‐Philippe Boulet, J Mark FitzGerald, Helen K. Reddel
2014· review· en· Current Opinion in Pulmonary Medicine· Medicine
machine prediction:candidate · noneconsensus · none
124
citations
affno abstractunlabeled
Near-Fatal Asthma
Ian Mitchell, Suzanne Tough, Lisa Semple, Francis H. Green, Patrick A. Hessel
2002· article· en· CHEST Journal· Medicine
machine prediction:candidate · noneconsensus · none
120
citations
affvenueunlabeled
Psychological Factors in Asthma
Ryan J. Van Lieshout, Glenda MacQueen
2008· article· en· Allergy Asthma and Clinical Immunology· Medicine
machine prediction:candidate · noneconsensus · none
120
citations

How this was built: Screen · Findings · About