Processes of Health Care Delivery, Education, and Provider Satisfaction in Acute Care Surgery: A Systematic Review
Bibliographic record
Abstract
In recent years, significant workload, high acuity, and complexity of emergency general surgery conditions have led hospitals to replace the traditional on-call model with dedicated acute care surgery (ACS) service models. A systematic search of Ovid, EMBASE, and MEDLINE was undertaken to examine the impact of ACS services on health-care delivery processes and cost, education, and provider satisfaction. From 1827 papers, reviewers identified 22 studies that met inclusion criteria and subsequently used The Evidence-Based Practice for Improving Quality method and Newcastle-Ottawa Scale to score quality and level of evidence. Most studies found an increase in daytime operating, improved patient transit from emergency department to operating room to home, and decreased length of stay. Higher and more diverse case volumes improved resident education and operative experience. ACS services enhanced the educational experience of residents on subspecialty services by offloading emergency work from those services. Finally, surgeons generally felt that ACS services improved job satisfaction, productivity, and billing. The ACS model has demonstrated improvement in timeliness of care, diversified case mix, decreased costs, improved trainee learning, and increased surgeon job satisfaction.
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 | 0.008 | 0.039 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.009 |
| Bibliometrics | 0.008 | 0.011 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".