Smallest detectable and minimal clinically important differences of rehabilitation intervention with their implications for required sample sizes using WOMAC and SF-36 quality of life measurement instruments in patients with osteoarthritis of the lower extremities
Bibliographic record
Abstract
OBJECTIVE: To discuss the concepts of the minimal clinically important difference (MCID) and the smallest detectable difference (SDD) and to examine their relation to required sample sizes for future studies using concrete data of the condition-specific Western Ontario and McMaster Universities Osteoarthritis Index (WOMAC) and the generic Medical Outcomes Study 36-Item Short Form (SF-36) in patients with osteoarthritis of the lower extremities undergoing a comprehensive inpatient rehabilitation intervention. METHODS: SDD and MCID were determined in a prospective study of 122 patients before a comprehensive inpatient rehabilitation intervention and at the 3-month followup. MCID was assessed by the transition method. Required SDD and sample sizes were determined by applying normal approximation and taking into account the calculation of power. RESULTS: In the WOMAC sections the SDD and MCID ranged from 0.51 to 1.33 points (scale 0 to 10), and in the SF-36 sections the SDD and MCID ranged from 2.0 to 7.8 points (scale 0 to 100). Both questionnaires showed 2 moderately responsive sections that led to required sample sizes of 40 to 325 per treatment arm for a clinical study with unpaired data or total for paired followup data. CONCLUSION: In rehabilitation intervention, effects larger than 12% of baseline score (6% of maximal score) can be attained and detected as MCID by the transition method in both the WOMAC and the SF-36. Effects of this size lead to reasonable sample sizes for future studies lying below n = 300. The same holds true for moderately responsive questionnaire sections with effect sizes higher than 0.25. When designing studies, assumed effects below the MCID may be detectable but are clinically meaningless.
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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 | 0.190 | 0.411 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| 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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".