Les soins d’urgence et l’oncologie
Bibliographic record
Abstract
Un Canadien sur deux développera un cancer au cours de sa vie. En ce sens, il est de plus en plus commun de soigner des usagers recevant des traitements contre le cancer (URTCC) dans les salles d’urgence. Les modalités de traitement offert aux URTCC sont dorénavant nombreuses et plus complexes. Il n’est plus seulement question de chimiothérapie, de radiothérapie ou de chirurgie. À ces modalités se sont ajoutées l’immunothérapie, la thérapie ciblée, l’hormonothérapie et la greffe de cellules souches. Il est d’ailleurs reconnu que la prise en charge de certains URTCC, comme ceux recevant de la chimiothérapie, se distingue des autres clientèles puisque des précautions doivent être mises en place lors de la manipulation des excrétas. Il importe donc de sensibiliser le personnel infirmier (c’est-à-dire les infirmières, les infirmières auxiliaires et les préposés aux bénéficiaires) quant à certaines particularités liées à la prise en charge d’un URTCC à la salle d’urgence. Cet article qui se veut à la fois instructif et ludique est le premier d’une série de deux.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.013 | 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".