Beyond just the operating room: characterizing the complete caseload of a tertiary acute care surgery service
Bibliographic record
Abstract
Background: Most studies evaluating acute care surgery (ACS) models of care for patients with emergency general surgery (EGS) conditions have focused on patients who undergo surgery while admitted under the care of the ACS service. The purpose of this study was to prospectively examine the case mix of admissions and consultations to an ACS service at a tertiary centre to identify the frequency and distribution of both operatively and nonoperatively managed EGS conditions. Methods: In this prospective cohort study, we evaluated consecutive patients assessed by the ACS team between July 1 and Aug. 31, 2015, at a large Canadian tertiary care centre. This included all consultations and outside hospital transfers. Diagnoses, demographic characteristics, comorbidities, intervention(s), complications, readmission and in-hospital death were captured. Results: The ACS team was involved in the care of 359 patients, 176 (49.0%) of whom were admitted under the direct care of the ACS team. Nonoperative care was indicated in 82 patients (46.6%) admitted to the ACS service and 151 (82.5%) of those admitted to a non-ACS service (p < 0.001). Bowel obstruction (37 patients [21.0%]) was the most common reason for admission, followed by wound/abscess (24 [13.6%), biliary disease (24 [13.6%]) and appendiceal disease (23 [13.1%]). Rates of 30-day return to the emergency department and readmission were 17.0% and 9.1%, respectively, and the in-hospital mortality rate was 1.7%. Conclusion: Acute care surgery teams care for a wide breadth of disease, a considerable amount of which is managed nonoperatively.
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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 | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| 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".