Impact of acute care surgery on timeliness of care and patient outcomes: a systematic review of the literature
Bibliographic record
Abstract
Background: Dedicated emergency general surgery (EGS) service models were developed to improve efficiency of care and patient outcomes. The degree to which the EGS model delivers these benefits is debated. We performed a systematic review of the literature to identify whether the EGS service model is associated with greater efficiency and improved outcomes compared to the traditional model. Methods: We searched MEDLINE, Embase, Scopus and Web of Science (Core Collection) databases from their earliest date of coverage through March 2017. Primary outcomes for efficiency of care were surgical response time, time to operation and total length of stay in hospital. The primary outcome for evaluating patient outcomes was total complication rate. Results: The EGS service model generally improved efficiency of care and patient outcomes, but the outcome variables reported in the literature varied. Conclusion: Development of standardized metrics and comprehensive EGS databases would support quality control and performance improvement in EGS systems.
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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 | 0.009 | 0.042 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.006 |
| Bibliometrics | 0.013 | 0.017 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| 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".