Development and Feasibility of a Scale to Assess Postoperative Recovery
Bibliographic record
Abstract
BACKGROUND: Good postoperative recovery is increasingly recognized as an important outcome after surgery. The authors created a new Post-operative Quality Recovery Scale (PQRS) that tracks multiple domains of recovery from immediate to long-term time periods in patients of varying ages, languages, and cultures. METHODS: The parameters of importance to both clinicians and patients were identified. After an initial pilot study of 133 patients, the PQRS was refined. It consists of six domains (physiologic, nociceptive, emotive, activities of daily living, cognitive, and overall patient perspective). An observational study of 701 patients was performed with the refined PQRS to assess its capacity to evaluate and track recovery and to discriminate between patients. It was conducted in eight countries and in five languages, involving patients more than or equal to 6 yr undergoing elective surgery with general anesthesia. Recovery was assessed before surgery and at multiple time periods postoperatively. Recovery was defined as return to baseline values or better. RESULTS: Seven hundred one patients completed the PQRS. Mean completion time was 4.8 (SD 2.8) min. Recovery scores improved with time. Physiologic recovery was complete in 34% of subjects by 40 min. By the third postoperative day, complete recovery was obtained in 11% of cases (all domains): 48.7% nociceptive, 81.8% emotive, 68.8% activities of daily living, and only 33.5% cognitive. Overall, 95.8% of the patients reported that they were "satisfied or totally satisfied" with their anesthetic care. CONCLUSION: The scores on the PQRS demonstrated an improvement over time, consistent with an expected recovery after surgery and anesthesia, and an ability to discriminate between individuals. Many patients had incomplete recovery by the third postoperative day.
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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 | 0.021 | 0.047 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".