Bibliographic record
Abstract
En santé, des professionnels peuvent se trouver à risque d'abuser de leurs prérogatives en négligeant les intérêts de leurs clients.Un conflit d'intérêts peut survenir notamment lorsque le praticien privilégie un intérêt secondaire, tel que son propre gain financier, au détriment d'un intérêt primaire ou d'une responsabilité professionnelle, comme l'intérêt du patient.Cette étude de cas, inspirée d'une histoire réelle en médecine vétérinaire, illustre le problème éthique par lequel des interventions de haute technicité utilisées en pratique s'avèrent fortement conseillées, même si elles ne sont pas nécessairement dans le meilleur intérêt du client.In health care, professionals may find themselves at risk of abusing their powers by neglecting the interests of their clients.In particular, a conflict of interest can arise when the practitioner focuses on a secondary interest, such as his own financial gain, at the expense of a primary interest or professional responsibility, such as the interest of the patient.This case study, inspired by a true story in veterinary medicine, illustrates the ethical problem by which high-tech interventions are used in practice, even if their use is not necessarily in the client's best interest.
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 | 0.011 | 0.023 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.020 | 0.028 |
| Scholarly communication | 0.009 | 0.005 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.013 | 0.010 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".