A Systematic Review of Postoperative Recovery Outcomes Measurements After Ambulatory Surgery
Bibliographic record
Abstract
In Brief BACKGROUND: Mortality and morbidity in ambulatory surgery are rare, and thus the patient's quality of life (i.e., the ability to resume normal activities after discharge home) should be considered one of the principle end-points after ambulatory surgery and anesthesia. We conducted a systematic review of the instruments to measure the quality of recovery of ambulatory surgical patients in order to advise on the selection of appropriate measures for research and quality assurance. METHODS: A systematic literature search of MEDLINE, EMBASE, CINAHL, HAPI, PsycINFO, Web of Science Search History, Biosys Previews Search, HealthStar, and ASSIA was performed to identify patient-based outcome measures to assess postoperative recovery from ambulatory anesthesia. The instruments were assessed for eight criteria: appropriateness, reliability, validity, responsiveness, precision, interpretability, acceptability, and feasibility. RESULTS: Seven articles met the inclusion criteria set for the review. The quality of the identified instruments was variable. CONCLUSION: Only one instrument, 40-item Quality of recovery score, fulfilled all eight criteria, however this instrument was not specifically designed for ambulatory surgery and anesthesia. IMPLICATIONS: Assessment of recovery after ambulatory surgery has become a remarkable outcome in many clinical studies. Through a systematic review, the published evidence related to postoperative recovery outcome measurement within 1 wk after ambulatory surgery was identified and validity, reliability, and responsiveness to change and clinical applicability of the instruments analyzed.
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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 | 0.008 | 0.041 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.008 | 0.005 |
| 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 | 0.002 | 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".