The impact of a dedicated Acute Care Surgical Service on the delivery of care for patients with general surgical emergencies
Bibliographic record
Abstract
Introduction: Acute Care Surgery (ACS) is a new model of emergency general surgery care developed to provide prompt, comprehensive, and evidence-based care to acutely ill non-trauma surgical patients. Our objective was to determine the impact of implementing ACS on efficiency of care (EOC) and patient outcomes. Methods: A retrospective review was performed for patients with acute appendicitis (AA) and biliary tract disease (BTD). EOC measures and patient outcomes were compared over two time periods: pre-ACS (2007) and ACS (2011). Results: n=1,229 patients were included in this study; n=507 (pre-ACS), and n=722 (ACS). Surgical response times and acquisition of imaging were significantly faster with ACS. Time to OR and total LOS were similar between cohorts. Similar rates of daytime operating were present. With ACS and AA, there were more perforations, more ORs were performed at night and patients were readmitted more frequently. Conclusions: Increased volumes of patients were seen with ACS, but surgical assessments and imaging were significantly faster. Inpatient EOC measures were unchanged with ACS; outcomes for AA were worse.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".