[Non-justified visits to emergency units. Proposal of differentiated care].
Bibliographic record
Abstract
OBJECTIVE: The number of visits to emergency units in public hospital settings in France increases every year. The adequation between admission to an SAU--Service d'accueil d'urgence (emergency unit) and the clinical status of the patient must be checked to improve handling upstream of the SAU. METHOD: A prospective study was conducted in the SAU of the University Hospital in Nantes to assess the proportion of patients who would benefit from direct hospitalisation, scheduled in a department of specialised or polyvalent medicine. RESULTS: This proportion was of 10%. Seventy-three percent of the patients were aged over 60. They were referred in 77% of cases by their treating physician and in 10.4% of cases by the physician on duty. Thirty-three percent of cases were non-specified organ diseases, 20% were dermatological affections, 12% broncho-pulmonary infections and in the same proportion rheumatological pathologies; other affections were rare. COMMENTS: The results of this study must be confirmed in a pilot study in which the general practitioner would refer any patient, that he would have sent to an SAU, directly to a medical department without passing through the SAU. To do so, using a cell phone, the practitioner would contact the hospital physician who would find a hospital bed. The impact of this new modality of hospitalisation on the SAU could be assessed in terms of the number of admissions avoided to the SAU.
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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 | 0.002 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| 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) | 0.016 | 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".