Predictors of patient satisfaction with anaesthesia and surgery care
Bibliographic record
Abstract
CONTEXT: Previous research has shown that most patients are satisfied with their anaesthetic care. For those who are not the causes may be multifactorial including dissatisfaction with surgical outcomes. OBJECTIVES: We aimed to identify whether quality of recovery after anaesthesia and surgery measured in multiple domains affects patient satisfaction. DESIGN: Sub-group analysis of previously published observational cohort study of quality of recovery after surgery (using the Postoperative Quality of Recovery Scale) was used to identify predictors of incomplete satisfaction 3 days after surgery. SETTING: Multicentre perioperative surgery. PATIENTS: Patients ≥6 years old, undergoing a variety of operation types and all receiving general anaesthesia. OBSERVATIONS: Of 701 patients, 573 completed the satisfaction question on day 3. Satisfaction was rated by a single five-point rating question. Patients were divided into two groups: 477 (83%) were completely satisfied and 96 (17%) were not completely satisfied. Multivariable logistic regression analysis was performed on preoperative and patient characteristics and recovery in five domains as follows: physiological, nociceptive (pain and nausea), emotive (anxiety and depression), activities of daily living and cognition. Recovery was defined as return to baseline values or better for all questions within each domain. RESULTS: Incomplete satisfaction was predicted by persistent pain or nausea at day 3 [OR 8.2 (95% CI 2.5 to 27), P<0.01] and incomplete satisfaction at day 1 [OR 28 (95% CI 10 to 77), P<0.01]. Paradoxically, incomplete satisfaction was less likely to occur if pain or nausea was present 15 min after surgery [OR 0.34 (95% CI 0.11 to 0.99), P<0.05] or at day 1 [OR 0.30 (95% CI 0.10 to 0.91), P=0.03]. Incomplete recovery in the other domains did not influence satisfaction. CONCLUSION: Of the recovery domains measured using the Postoperative Quality of Recovery Scale, only nociception (pain or nausea) contributed to incomplete satisfaction.
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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 | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".