Validation of the Self-Assessment of Treatment Questionnaire among Patients with Postherpetic Neuralgia
Bibliographic record
Abstract
Introduction. A five-item Self-Assessment of Treatment (SAT) was developed to assess improvement and satisfaction with treatment associated with the application of a novel high concentration 8% capsaicin topical patch in clinical trials in patients with postherpetic neuralgia (PHN). This study evaluated the item performance and psychometric properties of the SAT. Methods. The SAT, Brief Pain Inventory, SF-36v2, Short-Form McGill Pain Questionnaire, and Patient and Clinician Global Impression of Change (PGIC; CGIC) scores were measured in two 12-week Phase 3 clinical trials. Factor analysis assessed the underlying factor structure, followed by examination of the reliability and validity of the multi-item domain. Results. Pooled data from 698 patients completing SAT after 12 weeks of treatment were analyzed. A one-factor model combining three of the five items emerged as the optimal solution. Internal consistency reliability of this treatment efficacy factor was high (Cronbach's alpha = 0.89). Construct validity was demonstrated by moderate to high correlations with change in other study endpoints. SAT mean scores consistently discriminated between patient change groups defined by PGIC and CGIC. Conclusions. The measurement properties of the three-item version of SAT are valid and reliable for assessment of treatment with a high concentration capsaicin patch among patients with PHN.
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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 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".