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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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The International Journal of Tuberculosis and Lung Disease
Topic
Retraction
Abstract
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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.

154 results · 1 filter active ·
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20062025
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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.
154 works in the cohort · of 4,299,418page 3 of 4

Labels cover 0 of 154 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 154 of 154 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
TB and women: a call to action
Amrita Daftary, Jennifer Furin, Jennifer Zelnick, Nanditha Venkatesan, Karen R Steingart, Marina Smelyanskaya +25 more
2020· letter· en· The International Journal of Tuberculosis and Lung Disease· Medicine
machine prediction:candidate · noneconsensus · none
7
citations
affno abstractunlabeled
Impact of COVID-19 on TB detection in the private sector in Nepal
Rajesh Sah, U. K. Singh, Ram Prasad Mainali, Nathaly Aguilera Vasquez, Ahmad Khan
2021· article· en· The International Journal of Tuberculosis and Lung Disease· Economics, Econometrics and Finance
machine prediction:candidate · noneconsensus · none
5
citations
fundno affunlabeled
Standards for clinical trials for treating TB
Philipp du Cros, Jane Greig, J.-W. C. Alffenaar, Gail Brenda Cross, C. R. Cousins, Catherine Berry +34 more
2023· article· en· The International Journal of Tuberculosis and Lung Disease· Medicine
machine prediction:candidate · metaresearchconsensus · metaresearch
5
citations
affunlabeled
Global survey of national tuberculosis drug policies
Anita Paydar, Asy Mak, Hamdan Al Jahdali, Mirtha del Granado, R. Zaleskis, Nigor Mouzafarova +2 more
2011· article· en· The International Journal of Tuberculosis and Lung Disease· Medicine
machine prediction:candidate · noneconsensus · none
4
citations
affno abstractunlabeled
Engaging private pharmacies to help end TB in India
Ravdeep Gandhi, Khumanthem Deepak, Gajendra Kumar Verma, S. Chaubey, LIKITH KUMAR NUCHINA KUMAR, Joel Shyam Klinton +3 more
2022· article· en· The International Journal of Tuberculosis and Lung Disease· Mathematics
machine prediction:candidate · noneconsensus · none
3
citations
affaboutno abstractunlabeled
Canadian immigrants´ awareness and perceptions of TB infection and TB
Isdore Chola Shamputa, Duyên Thi Kim Nguyêñ, Tatum Burdo, Grace J. Đào, L. Gharbiya, M. Burns +3 more
2022· article· en· The International Journal of Tuberculosis and Lung Disease· Medicine
machine prediction:candidate · noneconsensus · none
2
citations
affunlabeled
TB preventive treatment among pregnant women with HIV
Teresa DeAtley, Yohhei Hamada, A. Baddeley, P. Werner, Avinash Kanchar, Matteo Zignol +1 more
2022· article· en· The International Journal of Tuberculosis and Lung Disease· Medicine
machine prediction:candidate · noneconsensus · none
2
citations
affno abstractunlabeled
Do rats pass the sniff test?
Madhukar Pai
2017· letter· en· The International Journal of Tuberculosis and Lung Disease· Veterinary
machine prediction:candidate · noneconsensus · none
1
citations
affunlabeled
A killer combination that must be stopped
Ramnath Subbaraman, Madhukar Pai
2015· letter· en· The International Journal of Tuberculosis and Lung Disease· Medicine
machine prediction:candidate · noneconsensus · none
1
citations
affno abstractunlabeled
Monitoring TB program performance
C. Andrew Basham, Pamela Orr
2022· letter· en· The International Journal of Tuberculosis and Lung Disease· Business, Management and Accounting
machine prediction:candidate · noneconsensus · none
1
citations
affunlabeled
Experiences of key populations in multidrug-resistant TB and HIV
Hlengiwe Nyilana, Karl Reis, Boitumelo Seepamore, Rubeshan Perumal, Allison Wolf, Karen Guzmán +13 more
2025· article· en· The International Journal of Tuberculosis and Lung Disease· Medicine
machine prediction:candidate · noneconsensus · none
0
citations
affno abstractunlabeled
In reply
James C. Johnston, Faiz Ahmad Khan, David W. Dowdy
2015· letter· en· The International Journal of Tuberculosis and Lung Disease· Medicine
machine prediction:candidate · noneconsensus · none
0
citations
affno abstractunlabeled
Pulmonary and pleural TB prevalence in pregnant women
Paulo Ranaivomanana, Astrid M. Knoblauch, M. C. Razafimahatratra, Antso Hasina Raherinandrasana, Simon Grandjean Lapierre, Perlinot Herindrainy +3 more
2021· letter· en· The International Journal of Tuberculosis and Lung Disease· Medicine
machine prediction:candidate · noneconsensus · none
0
citations
affno abstractunlabeled
Response to ‘Rethinking latent TB? Think again’
Marcel A. Behr, Paul H. Edelstein, Lalita Ramakrishnan
2025· article· en· The International Journal of Tuberculosis and Lung Disease· Medicine
machine prediction:candidate · noneconsensus · none
0
citations
affno abstractunlabeled
In reply 1
L Mota, K Al-Efraij, Jonathon R. Campbell, Victoria J. Cook, James C. Johnston
2016· letter· en· The International Journal of Tuberculosis and Lung Disease· Health Professions
machine prediction:candidate · noneconsensus · none
0
citations

How this was built: Screen · Findings · About