Interest and applicability of acute care surgery among surgeons in Quebec: a provincial survey
Bibliographic record
Abstract
BACKGROUND: Acute care surgery (ACS) comprises trauma and emergency surgery. The purpose of this new specialty is to involve trauma and nontrauma surgeons in the care of acutely ill patients with a surgical pathology. In Quebec, few acute care surgery services (ACSS) exist, and the concept is still poorly understood by most general surgeons. This survey was meant to determine the opinions and interest of Quebec general surgeons in this new model. METHODS: We created a bilingual electronic survey using a Web interface and sent it by email to all surgeons registered with the Association québécoise de chirurgie. A reminder was sent 2 weeks later to boost response rates. RESULTS: The response rate was 36.9%. Most respondents had academic practices, and 16% worked in level 1 trauma centres. Most respondents had a high operative case load, and 66% performed at least 10 urgent general surgical cases per month. Although most (88%) thought that ACS was an interesting field, only 45% were interested in participating in an ACSS. Respondents who deemed this concept least applicable to their practices were more likely to be working in nonacademic centres. CONCLUSION: Despite a strong interest in emergency general surgery, few surgeons were interested in participating in an ACSS. This finding may be explained by lack of comprehension of this new model and by comfort with traditional practice. We aim to change this paradigm by demonstrating the feasibility and benefits of the new ACSS at our centre in a follow-up study.
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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".