Allocating operating room resources to an acute care surgery service does not affect wait-times for elective cancer surgeries: a retrospective cohort study
Bibliographic record
Abstract
INTRODUCTION: Acute care surgical services provide timely comprehensive emergency general surgical care while optimizing the use of limited resources. At our institution, 50% of the daily dedicated operating room (OR) time allocated to the Acute Care Emergency Surgery Service (ACCESS) came from previous elective general surgery OR time. We assessed the impact of this change in resource allocation on wait-times for elective general surgery cancer cases. METHODS: We retrospectively reviewed adult patients who underwent elective cancer surgeries in the pre-ACCESS (September 2009 to June 2010) and post-ACCESS (September 2010 to June 2011) eras. Wait-times, calculated as the time between booking and actual dates of surgery, were compared within assigned priority classifications. Categorical and continuous variables were compared using chi-square and Mann-Whitney U tests respectively. RESULTS: A total of 732 cases (367 pre-ACCESS and 365 post-ACCESS) were identified, with no difference in median wait-times (25 versus 23 days) between the eras. However, significantly fewer cases exceeded wait-time targets in the post-ACCESS era (p <0.0001). There was a significant change (p = 0.027) in the composition of cancer cases, with fewer breast cancer operations (22% versus 28%), and more colorectal (41% versus 32%) and hepatobiliary cancer cases (5% versus 2%) in the post-ACCESS era. CONCLUSION: These results suggest that shifting OR resources towards emergency surgery does not affect the timeliness of surgical cancer care. This study may encourage more centres to adopt acute care surgical services alongside their elective or subspecialty practices.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".