Validity and Reliability of the Iranian Version of the Quality of Recovery-40 Questionnaire
Bibliographic record
Abstract
BACKGROUND: Recovery after anesthesia and surgery is a complex process and depends on many factors such as patient, anesthesia and surgery conditions as well pre-existing comorbidities. OBJECTIVES: The aims of this study were to translate the 40-item quality of recovery score (QoR-40) into Persian and evaluate its psychometric properties in Iranian patients. PATIENTS AND METHODS: We enrolled patients candidate for elective general surgery undergoing general anesthesia from July 2013 to December 2013 at Shahid Rajaee Hospital, Qazvin, Iran. Translation was performed based on Beaton's and Bullinger's recommendations. Estimates of internal consistency, test-retest reliability, concurrent validity, predictive validity and clinical validity were performed. RESULTS: All estimates of internal consistency were high (Cronbach's alpha = 0.89 for global estimates, subscales between 0.89 and 0.93). All test-retest scores and subscales were between 0.71 and 0.88.The correlation with a recovery visual analogue scale was 0.51, and all subscales correlated significantly with comparable subscales of the SF-36. An exploratory factor analysis found five-components and explained 52% of the variance. A confirmatory factor analysis based on the five-components, yielded good fit statistics (CFI = 0.93). CONCLUSIONS: Overall, the Persian version of the QoR-40 was both conceptually and linguistically equivalent to the original English QoR-40. This study revealed that the Persian version of the QoR-40 is a valid and reliable instrument to assess the recovery quality in Iranian patients after surgery.
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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 | 0.006 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".