Évaluation d'un outil de formation et d’aide à la prescription à destination des médecins généralistes landais sur les soins palliatifs gériatriques à domicile : une étude interventionnelle
Bibliographic record
Abstract
Background: a majority of the elderly wish to end their life at home. Only a quarter of them do. Multiple studies have identified obstacles to geriatric palliative home care. One of them is a formation need amongst general practitioners. Aims: evaluate the effect on clinical case-based multiple-choice test grades of an internet palliative care training and anticipatory prescribing assistance tool. We also evaluated the effect of the tool on the multiple-choice exam answers about referring patients to an emergency department, hospitalization rates and home care rates. Method: we conducted a prospective controlled randomized study between March 2023 and May 2023 on Landais general practitioners. Participants did a first session of a multiple-choice exam, graded out of 100. The exam also asked to decide whether to refer the patients to an emergency department, hospitalize or choose home care. We then provided the tool to the test group. Both groups then took the exam a second time. Data was extracted from the DragonSurvey.com website, treated with Excel® software. Statistical analysis was performed on Prism® software. Results: 32 questionnaires were analyzed. The test group saw their grade improve by 5,96 (p=0,024). The control group saw their grade improve by 4,29 (p=0,14). Emergency department reference, hospitalization or home care rates were not modified. Conclusion: performing progressive clinical case-based questions brings good practice. Palliative care must be developed through network expansion, training and tool development.
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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 | 0.014 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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".