The Test–Retest Reliability of Pain Outcome Measures in People With Phantom Limb Pain
Bibliographic record
Abstract
OBJECTIVES: To quantify the test-retest reliability of 3 patient-reported outcome measures of pain for people living with phantom limb pain (PLP) and assess the impact of test-retest errors on future research and clinical decisions. METHODS: Thirty-nine participants (30 males), mean (SD) age: 55 (16), mean (SD) years postamputation: 6.8 (8.3), reported their PLP levels on a visual analogue scale (VAS) for pain intensity, the revised short-form McGill Pain Questionnaire (SF-MPQ-2), and a pain diary, on 2 occasions 7 to 14 days apart. Mean systematic change, within-subjects SD, limits of agreement (LOA), coefficient of variation, and the intraclass correlation coefficient (ICC) were quantified alongside their respective 95% confidence intervals (95% CIs). RESULTS: Systematic learning effects (mean changes) were not clinically relevant across the VAS, SF-MPQ-2, and pain diary. Within-subject SDs (95% CI) were 11.8 (9.6-15.3), 0.9 (0.7-1.2), and 8.6 (6.9-11.5), respectively. LOA (95% CI) were 32.6 (26.5-42.4), 2.5 (2-3.3), and 23.9 (19.2-31.8), respectively. ICCs (95% CI) were 0.8 (0.6-0.9), 0.8 (0.7-0.9), and 0.9 (0.8-0.9), respectively, but may have been inflated by sample heterogeneity. The test-retest errors allowed detection of clinically relevant effect sizes with feasible sample sizes in future studies, but individual errors were large. DISCUSSION: For people with PLP, a pain intensity VAS, the SF-MPQ-2, and a pain diary show an acceptable level of intersession reliability for use in future clinical trials with feasible sample sizes. Nevertheless, the random error observed for all 3 of the pain outcome measures suggests they should be interpreted with caution in case studies and when monitoring individuals' clinical status and progress.
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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 | 0.019 | 0.045 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".