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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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Nanoparticle-Based Drug Delivery
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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.

1,356 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.
1,356 works in the cohort · of 4,299,418page 2 of 28

Labels cover 3 of 1,356 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 1,356 of 1,356 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.

afffundunlabeled
Effect of removing Kupffer cells on nanoparticle tumor delivery
Anthony J. Tavares, Wilson Poon, Yi-Nan Zhang, Qin Dai, Rickvinder Besla, Ding Ding +6 more
2017· article· en· Proceedings of the National Academy of Sciences· Materials Science
machine prediction:candidate · noneconsensus · none
282
citations
afffundgpt · no categoryopus · no categorymodels agree
Tailoring nanoparticle designs to target cancer based on tumor pathophysiology
Edward A. Sykes, Qin Dai, Christopher D. Sarsons, Juan Chen, Jonathan V. Rocheleau, David Hwang +4 more
2016· article· en· Proceedings of the National Academy of Sciences· Materials Science
machine prediction:candidate · noneconsensus · none
280
citations
afffundno abstractunlabeled
Why nanoparticles prefer liver macrophage cell uptake in vivo
Wayne Ngo, Sara Ahmed, Colin Blackadar, Bram Bussin, Qin Ji, Stefan M. Mladjenovic +2 more
2022· review· en· Advanced Drug Delivery Reviews· Materials Science
machine prediction:candidate · noneconsensus · none
248
citations
affunlabeled
Hyperthermia-induced drug targeting
Jonathan P. May, Shyh‐Dar Li
2013· review· en· Expert Opinion on Drug Delivery· Materials Science
machine prediction:candidate · noneconsensus · none
226
citations
affno abstractunlabeled
A translational framework to DELIVER nanomedicines to the clinic
Paul Joyce, Christine Allen, Marı́a José Alonso, Marianne Ashford, Michelle S. Bradbury, Matthieu Germain +15 more
2024· review· en· Nature Nanotechnology· Materials Science
machine prediction:candidate · noneconsensus · none
219
citations
affno abstractunlabeled
The mechanisms of nanoparticle delivery to solid tumours
Luan N. Nguyen, Wayne Ngo, Zachary P. Lin, Shrey Sindhwani, Presley MacMillan, Stefan M. Mladjenovic +1 more
2024· article· en· Nature Reviews Bioengineering· Materials Science
machine prediction:candidate · noneconsensus · none
217
citations
affunlabeled
Polymeric micelles for drug targeting
Abdullah Mahmud, Xiao-Bing Xiong, Hamidreza Montazeri Aliabadi, Afsaneh Lavasanifar
2007· review· en· Journal of drug targeting· Materials Science
machine prediction:candidate · noneconsensus · none
207
citations

How this was built: Screen · Findings · About