Impact of an Outpatient Appendectomy Protocol on Clinical Outcomes and Cost: A Case-Control Study
Bibliographic record
Abstract
BACKGROUND: Although elective outpatient surgery is commonplace, surgeons remain hesitant to discharge patients the same day after emergent surgery. We created a formal protocol to select patients for early discharge after laparoscopic appendectomy for acute appendicitis, and we assessed its safety and potential cost savings. STUDY DESIGN: We matched patients who were discharged early from the recovery room with similar patients from a control group on the basis of age ± 3 years, presence or absence of a comorbidity, laparoscopic procedure, and nonperforated appendicitis; we compared them to assess the impact of early discharge on morbidity, return visits to the emergency room, and total cost incurred by our institution. RESULTS: During the first year of our protocol, 72 of 161 (45%) patients who presented with acute appendicitis and underwent appendectomy were discharged early, with a median post-operative length of stay of 4.7 hours. When compared with matched controls, patients discharged early had similar complication rates (4.3% early group vs 7.1%, p = 0.72) and number of postoperative visits to the emergency room (11.4% vs 11.4%, p = 0.8), but had a reduced median length of stay (4.7 vs 16.2 hours, p < 0.001) and an average reduction in cost of $323.46 per patient. CONCLUSIONS: Adoption of a protocol to select patients for early discharge after laparoscopic appendectomy resulted in a 45% reduction in the need for in-hospital beds, with no negative impact on return visits to the emergency room or number of complications. This translates to an approximate savings of $323 per patient when compared with standard care.
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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 | 0.011 | 0.030 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 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".