Minimum Clinically Important Difference in SF-36 Scores for Use in Degenerative Cervical Myelopathy
Bibliographic record
Abstract
STUDY DESIGN: Post-hoc analysis of 606 patients enrolled in the AOSpine CSM-NA or CSM-I prospective, multicenter cohort studies. OBJECTIVE: The aim of this study was to determine the minimum clinically important difference (MCID) in SF-36v2 Physical Component Summary (PCS) and Mental Component Summary (MCS) scores in patients undergoing surgery for degenerative cervical myelopathy (DCM). SUMMARY OF BACKGROUND DATA: There has been a shift toward focus on patient-reported outcomes (PROs) in spine surgery. However, the numerical scores of PROs lack immediate clinical meaning. The MCID adds a dimension of interpretability to PRO scales; by defining the smallest change, a patient would consider meaningful. METHODS: The MCID of the SF-36v2 PCS and MCS were determined by distribution- and anchor-based methods comparing preoperative to 12-month scores. Distribution-based approaches included calculation of the half standard deviation and standard error of measurement (SEM). Change in Neck Disability Index (NDI) served as the anchor: "worse" (ΔNDI>7.5); "unchanged" (7.5≥ΔNDI>-7.5); "slightly improved" (-7.5≥ΔNDI>-15); and "markedly improved" (ΔNDI ≤-15). Receiver operating characteristic (ROC) analysis was performed to determine the change score for the MCID with even sensitivity and specificity to distinguish patients who were "slightly improved" versus "unchanged" on the NDI. RESULTS: The MCID for the SF-36v2 PCS and MCS were 4.6 and 6.8 by half standard deviation and 2.9 and 4.3 by SEM, respectively. By ROC analysis, the MCID was 3.9 for the SF-36v2 PCS score and 3.2 for the SF-36v2 MCS score. Using a cutoff of 4 points, the SF-36v2 PCS had a sensitivity of 72.2% and specificity of 68.1%, and MCS 61.9% and 64.6%, respectively, in separating patients who were "markedly improved" or "slightly improved" from those who were "unchanged" or "worse." CONCLUSION: We found the MCID of the SF-36v2 PCS and MCS to be 4 points. This will facilitate use of the SF-36v2 as an outcome in future studies of DCM. LEVEL OF EVIDENCE: 3.
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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 | 0.004 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".