Bibliographic record
Abstract
Chaque anne ´e, des milliers de Canadiennes et de Canadiens apprennent qu'ils souffrent d'un cancer.La plupart d'entre nous connaissons quelqu'un-un ami, un colle `gue ou un membre de notre famille-qui a rec ¸u un diagnostic de cette maladie.Le simple mot « cancer » peut provoquer une re ´action e ´motive ou `s'entreme ˆlent peur, anxie ´te ´et tristesse.Aujourd'hui, toutefois, la majorite ´des personnes qui survivent a `un cancer peuvent s'attendre a `franchir le seuil des cinq ans 1,2 .Pour citer la D re Lynn Gerber, me ´decin, le cancer est passe ´de « maladie fatale aigue ¨» a `« trouble complexe, chronique et courant » 3(p.xiv) .Physiotherapy Canada publiera une se ´rie spe ´ciale sur l'oncologie, qui comprendra au moins un article sur ce sujet dans quelques-unes des prochaines parutions.Cette se ´rie d'articles mettra en lumie `re la diversite ´des besoins, en fonction des types de cancer.Elle se penchera sur les nouvelles interventions et approches de traitement et offrira a `la fois de l'information sur l'e ´tat des faits probants issus de la recherche et des renseignements de premie `re main sur la disponibilite ´des services et programmes spe ´cialise ´s offerts au Canada.
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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 | 0.011 | 0.049 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.004 | 0.008 |
| Scholarly communication | 0.009 | 0.007 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.005 | 0.005 |
| Insufficient payload (model declined to judge) | 0.008 | 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".