Minimal important difference and patient acceptable symptom state for the Numerical Rating Scale (NRS) for pain and the Patient-Rated Wrist/Hand Evaluation (PRWHE) for patients with osteoarthritis at the base of thumb
Bibliographic record
Abstract
Abstract Background The Numerical Rating Scale (NRS) and Patient-rated wrist/hand evaluation (PRWHE) are patient-reported outcomes frequently used for evaluating pain and function of the wrist and hand. The aim of this study was to determine thresholds for minimal important difference (MID) and patient acceptable symptom state (PASS) for NRS pain and PRWHE instruments in patients with base of thumb osteoarthritis. Methods Fifty-two patients with symptomatic base of thumb osteoarthritis wore a splint for six weeks before undergoing trapeziectomy. NRS pain (0 to 10) and PRWHE (0 to 100) were collected at the time of recruitment (baseline), after splint immobilization prior to surgery, and at 3, 6, 9 and 12 months after surgery. Four anchor-based methods were used to determine MID for NRS pain and PRWHE: the receiver operating characteristics (ROC) curve, the mean difference of change (MDC), the mean change (MC) and the predictive modelling methods. Two approaches were used to determine PASS for NRS pain and PRWHE: the 75th percentile and the ROC curve methods. The anchor question for MID was the change perceived by the patient compared with baseline; the anchor question for PASS was whether the patient would be satisfied if the condition were to stay similar. The correlation between the transition anchor at baseline and the outcome at all time points combined was calculated using the Spearman’s rho analysis. Results The MID for NRS pain was 2.5 using the ROC curve method, 2.0 using the MDC method, 2.8 using the MC method, and 2.5 using the predictive modelling method. The corresponding MIDs for PRWHE were 22, 24, 10, and 20. The PASS values for NRS pain and PRWHE were 2.5 and 30 using the ROC curve method, and 2.0 and 22 using the 75th percentile method, respectively. The area under curve (AUC) analyses showed excellent discrimination for all measures. Conclusion We found credible MID estimates for NRS and PRWHE (including its subscales), although the MID estimates varied depending on the method used. The estimates were 20-30% of the range of scores of the instruments. The cut-offs for MID and PASS showed good or excellent discrimination, lending support for their use in future studies. Trial registration This clinimetrics study was approved by the Helsinki University ethical review board (HUS1525/2017).
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.004 | 0.018 |
| 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 | 0.000 | 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".