Results of Introducing a Rapid Recovery Program for Total Abdominal Hysterectomy
Bibliographic record
Abstract
OBJECTIVE: To review the impact of implementing a rapid recovery protocol (RRP) for patients undergoing abdominal hysterectomy. SETTING: Metropolitan teaching hospital. POPULATION: Women undergoing abdominal hysterectomy for non-malignant indications. METHODS: We conducted a retrospective review of consecutive cases performed during study periods before and after the introduction of an elective rapid recovery program emphasizing regional anesthesia. To control for universal improvements in medical practice, charts from a comparable local hospital without an RRP were also reviewed. RESULTS: 400 charts were reviewed and 366 cases met inclusion criteria and had sufficient information. Patients were well matched for demographic and medical variables between the study periods and between the institutions. The median length of stay (LOS) fell dramatically from 3 (range 1-12) days prior to RRP introduction to 1 (range 1-17) day after RRP (p < 0.001). LOS among patients at the 'control' institution remained unchanged at 3 days during the same time frame, indicating that external pressures contributed minimally to the observed changes. There were no significant differences in estimated blood loss, duration of surgery, or complication rate between the groups in either time period. CONCLUSIONS: Introducing a rapid recovery program was associated with shorter hospitalization and did not appear to compromise surgical outcome.
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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 | 0.003 | 0.022 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".