Série « Prévenir la thrombose »: Sensibiliser les professionnels de la santé à la thrombose liée au cancer.
Bibliographic record
Abstract
Le présent article est le troisième d’une série intitulée « Prévenir la thrombose », qui vise à mieux faire connaître la thrombose associée au cancer (TAC) et ainsi améliorer les connaissances des malades et des soignants à ce sujet, l’état des patients et, en bout de ligne, réduire le fardeau de cette comorbidité. Les deux premiers articles, Importance de la thromboembolie veineuse liée au cancer (Sardo et al., 2021) et Ce que les personnes atteintes de cancer veulent savoir (Bayadinova et al., 2022) ont décrit la TAC, le manque de connaissances sur cette affection touchant la population atteinte de cancer, puis présenté des stratégies de sensibilisation et d’éducation destinées aux malades. L’objectif de ce troisième article est de sensibiliser les professionnels de la santé à la thrombose liée au cancer, en dégageant les lacunes relatives au savoir du personnel médical, en suggérant des outils pour identifier les patients à plus haut risque et en proposant des stratégies et des ressources pour sensibiliser davantage le personnel soignant.
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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 | 0.007 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.020 | 0.002 |
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".