Validation of the scale on Satisfaction of Adolescents with Postoperative pain management-idiopathic Scoliosis (SAP-S)
Bibliographic record
Abstract
BACKGROUND: Spinal fusion is a common orthopedic surgery in children and adolescents and is associated with high pain levels postoperatively. If the pain is not well managed, negative outcomes may ensue. To our knowledge, there is no measure in English that assesses patient's satisfaction with postoperative pain management following idiopathic scoliosis surgery. The aim of the present study was to assess the psychometric properties of the satisfaction subscale of the English version of the Satisfaction of Adolescents with Postoperative pain management - idiopathic Scoliosis (SAP-S) scale. METHODS: Eighty-two participants aged 10-18 years, who had undergone spinal fusion surgery, fully completed the SAP-S scale at 10-14 days postdischarge. Construct validity was assessed through a principal component analysis using varimax rotation. RESULTS: Principal component analysis indicated a three-factor structure of the 13-item satisfaction subscale of the SAP-S scale. Factors referred to satisfaction regarding current medication received (Factor 1), actions taken by nurses and doctors to manage pain (Factor 2) and information received after surgery (Factor 3). Cronbach's alpha was 0.91, showing very good internal consistency. Data on satisfaction and clinical outcomes were also reported. CONCLUSION: The SAP-S is a valid and reliable measure of satisfaction with postoperative pain management that can be used in both research and clinical settings to improve pain management practices. Although it was developed and validated with adolescents who had undergone spinal fusion surgery, it can be used, with further validation, to assess adolescents' satisfaction with pain management in other postoperative contexts.
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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 | 0.005 | 0.010 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".