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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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International Journal of Laboratory Hematology
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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.

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.

136 results · 1 filter active ·
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20072025
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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.
136 works in the cohort · of 4,299,418page 1 of 3

Labels cover 0 of 136 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 136 of 136 works in this cohort. Predictions are machine_predicted_unvalidated teacher distillation outputs. Candidate is the union; consensus is the intersection.

affunlabeled
Review of D‐dimer testing: Good, Bad, and Ugly
Lori‐Ann Linkins, S. Takach Lapner
2017· review· en· International Journal of Laboratory Hematology· Medicine
distilled prediction:candidate · noneconsensus · none
286
citations
affunlabeled
Red blood cell morphology
Jes Ford
2013· review· en· International Journal of Laboratory Hematology· Medicine
distilled prediction:candidate · metaepi_narrow+insufficient_payloadconsensus · none
240
citations
affunlabeled
Flow cytometry data analysis: Recent tools and algorithms
Sebastiano Montante, Ryan R. Brinkman
2019· review· en· International Journal of Laboratory Hematology· Biochemistry, Genetics and Molecular Biology
distilled prediction:candidate · metaepi_narrowconsensus · none
79
citations
afffundunlabeled
How I investigate for bleeding disorders
Catherine P.M. Hayward
2018· review· en· International Journal of Laboratory Hematology· Medicine
distilled prediction:candidate · noneconsensus · none
54
citations
affunlabeled
Applied machine learning in hematopathology
Taher Dehkharghanian, Youqing Mu, Hamid R. Tizhoosh, Clinton J.V. Campbell
2023· review· en· International Journal of Laboratory Hematology· Computer Science
distilled prediction:candidate · metaepi_narrowconsensus · none
25
citations
affaboutunlabeled
Critical values in Hematology
A. McFarlane, Bengisu Aslan, Anne Raby, G. Bourner, Ruth Padmore
2014· article· en· International Journal of Laboratory Hematology· Medicine
distilled prediction:candidate · metaresearchconsensus · none
22
citations
fundno affunlabeled
Congenital erythrocytosis
M. F. McMullin
2016· review· en· International Journal of Laboratory Hematology· Medicine
distilled prediction:candidate · insufficient_payloadconsensus · none
20
citations

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