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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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Intensive Care 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.

1,011 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.
1,011 works in the cohort · of 4,299,418page 17 of 21

Labels cover 0 of 1,011 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 1,011 of 1,011 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
The CVC and CRBSI: don’t use it and lose it!
Kevin B. Laupland, Desponia Koulenti, Carole Schwebel
2017· letter· en· Intensive Care Medicine· Health Professions
machine prediction:candidate · noneconsensus · none
4
citations
affno abstractunlabeled
The medical management of acute respiratory distress syndrome
Idunn S. Morris, Marcelo B. P. Amato, ELIAS BAEDORF KASSIS, Giacomo Bellani, Carolyn S. Calfee, Leo Heunks +9 more
2025· article· en· Intensive Care Medicine· Medicine
machine prediction:candidate · noneconsensus · none
4
citations
fundno affno abstractunlabeled
Helmet trials: resolving the puzzle
Yaseen M. Arabi, Bhakti K. Patel, Massimo Antonelli
2023· letter· en· Intensive Care Medicine· Medicine
machine prediction:candidate · noneconsensus · none
4
citations
affno abstractunlabeled
On the “bubble” of burnout’s prevalence estimates
Éric Laurent, Irvin Sam Schonfeld, Renzo Bianchi, Laura Hawryluck, Peter G. Brindley
2018· letter· en· Intensive Care Medicine· Health Professions
machine prediction:candidate · metaresearchconsensus · none
4
citations
affno abstractunlabeled
Have we averted deaths using venoarterial ECMO?
Matthieu Schmidt, Hannah Wunsch, Daniel Brodie
2018· article· en· Intensive Care Medicine· Engineering
machine prediction:candidate · noneconsensus · none
4
citations
affno abstractunlabeled
Some remaining important questions after LUNG SAFE
Didier Dreyfuss, Stèphane Gaudry, Fabiana Madotto, John G. Laffey
2017· letter· en· Intensive Care Medicine· Medicine
machine prediction:candidate · noneconsensus · none
3
citations

How this was built: Screen · Findings · About