Caractérisation des unités de soins aigus chirurgicaux au sein des départements de chirurgie générale au Canada
Bibliographic record
Abstract
Introduction : The acute care surgery (ACS) units are dedicated to the prompt management of surgical emergencies. It is a systemic way of organizing on-call services to diminish conflict between urgent care and elective obligations. The aim of this study was to define the characteristics of an ACS unit and to find common criteria in units with reported good functioning. Methods : As of July 1st 2014, 22 Canadian hospitals reported having an ACS unit. A survey with questions about the organization of the ACS units, the population it serves, the number of emergencies and trauma cases treated per year, and the satisfaction about the implementation of this ACS unit was sent to those hospitals. Results : The survey’s response rate was 73%. The majority of hospitals were tertiary or quaternary centers, served a population of more than 200 000 and had their ACS unit for more than three years. The median number of surgeons participating in an ACS unit was 8.5 and the majority were doing seven day rotations. The median number of operating room days was 2.5 per week. Most ACS units (85%) had an estimated annual volume of more than 2500 emergency consultations (including both trauma and non-trauma) and 80% operated over 1000 cases per year. Nearly all the respondents (94%) were satisfied with the implementation of the ACS unit in their hospital. Conclusion : Most surgeons felt that the implementation of an ACS unit resulted in positive outcomes. However, there should be a sizeable catchment population and number of surgical emergencies to justify the resulting financial and human resources.
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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 | 0.000 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 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".