Implementation of the Minimum Clinically Important Difference for the Neck Disability Index Is Often Problematic
Bibliographic record
Abstract
STUDY DESIGN: Systematic review. OBJECTIVE: To determine the incidence of inappropriate or uncertain implementation of the minimally important clinical difference (MCID) for the neck disability index (NDI). SUMMARY OF BACKGROUND DATA: The NDI consists of 10 items that yield a total score out of 50, but some users double the scale to report total scores out of 100. The most used MCID for the NDI is 7.5 out of 50. Implementation of the MCID can be problematic if users are not attentive to the scale of the NDI. METHODS: We performed a methodological review of studies that cited the MCID for the NDI. We defined appropriate implementation as the congruent magnitude of the scales used for NDI data and the MCID. We evaluated study characteristics associated with appropriate implementation using multivariable logistic regression. RESULTS: Among 163 included studies, twenty (12%) reported a 0 to 50 scale for the NDI, 66 (40%) reported a 0 to 100 scale, and the remaining 77 (47%) did not report which scale was used. Fifty-seven (35%) reported an MCID of 7.5, 37 (23%) reported an MCID of 15, and the remaining 69 (42%) did not report which value of the MCID used. Appropriate implementation of the MCID occurred in 39 studies (24%), whereas implementation was inappropriate in 16 (10%) and uncertain due to poor reporting in 108 (66%). Studies published more recently (OR 1.20 per yr, 95% CI 1.02-1.40, P =0.03) and studies that were RCTs (OR 4.85, 95% CI 1.25-18.79, P =0.02) had greater odds of being associated with appropriate implementation. CONCLUSIONS: Inappropriate implementation of the MCID for the NDI is problematic and occurs often, and uncertain implementation due to poor reporting is also common. Evidence users should be cautious when interpreting studies that implement the NDI, and should consider whether the magnitude of the scales used for the NDI and the MCID are congruent.
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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 | 0.132 | 0.415 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.010 | 0.011 |
| Bibliometrics | 0.009 | 0.011 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".