Corrigendum: Management of Cancer-Associated Thrombosis: Unmet Needs and Future Perspectives
Bibliographic record
Abstract
Correction to: Management of Cancer-Associated Thrombosis: Unmet Needs and Future Perspectives TH Open 2021; 05(03): e376-e386 DOI: 10.1055/s-0041-1736037 Corrigendum It has been brought to the publisher's attention that the name of Grégoire Le Gal was incorrectly tagged in the metadata of the above article published in TH Open , Volume 5, Issue 3, e376–e386, on August 31, 2021 (doi: 10.1055/s-0041-1736037 ). The given name and surname tagging has now been corrected in the article metadata. Publication History Article published online: 10 July 2025 © 2025. The Author(s). This is an open access article published by Thieme under the terms of the Creative Commons Attribution License, permitting unrestricted use, distribution, and reproduction so long as the original work is properly cited. (https://creativecommons.org/licenses/by/4.0/) Georg Thieme Verlag KG Oswald-Hesse-Straße 50, 70469 Stuttgart, Germany Bibliographical Record Anna Falanga, Grégoire Le Gal, Marc Carrier, Hikmat Abdel-Razeq, Cihan Ay, Andrés J. Muñoz Martin, Ana Thereza Cavalcanti Rocha, Giancarlo Agnelli, Ismail Elalamy, Benjamin Brenner. Corrigendum: Management of Cancer-Associated Thrombosis: Unmet Needs and Future Perspectives. TH Open 2021; 05: s00451810091. DOI: 10.1055/s-0045-1810091
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.044 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.013 | 0.016 |
| Insufficient payload (model declined to judge) | 0.028 | 0.022 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".