Performance of baseline quartile-stratified minimal clinically important difference estimates was superior to individual minimal clinically important difference estimates when compared with a gold standard comparator of important change
Bibliographic record
Abstract
ABSTRACT: A variety of minimal clinically important difference (MCID) estimates are available to distinguish subgroups with differing outcomes. When a true gold standard is absent, latent class growth curve analysis (LCGC) has been proposed as a suitable alternative for important change. Our purpose was to evaluate the performance of individual and baseline quartile-stratified MCIDs. The current study included data from 346 persons with baseline and 12-month postoperative outcome data from KASTPain, a no-effect randomized clinical trial conducted on persons with knee arthroplasty and pain catastrophizing. Subgroup trajectories from LCGC were used as a gold standard comparator. Minimal clinically important difference-specific trajectories of recovery were calculated for the Western Ontario and McMaster Universities Osteoarthritis Index (WOMAC) Pain, Disability and EuroQol-5 Dimension Visual Analogue Scale of self-reported health. The latent Kappa (K l ) chance-corrected agreement between MCIDs and LCGCs were estimated to indicate which MCID method was best at detecting important change. For all 3 outcomes, the average latent class probabilities ranged from 0.90 to 0.99, justifying the use of LCGCs as a gold standard. The K l for LCGC and individual MCIDs ranged from 0.21 (95% CI = 0.13, 0.28) to 0.52 (95% CI = 0.41, 0.66). Baseline quartile-stratified K l for WOMAC Pain and Disability were 0.85 (95% CI = 0.78, 0.92) and 0.74 (95% CI = 0.68, 0.83), respectively. Classification errors in individual MCID estimates most likely result from ceiling effects. Minimal clinically important differences calculated for each baseline quartile are superior to individually calculated MCIDs and should be used when latent class methods are not available. Use of individual MCIDs likely contribute substantial error and are discouraged for clinical application.
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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 | 0.084 | 0.141 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| 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".