The Modified – Modified Schober Test for range of motion assessment of lumbar flexion in patients with low back pain: A study of criterion validity, intra- and inter-rater reliability and minimum metrically detectable change
Bibliographic record
Abstract
PURPOSE: The objective was to estimate the psychometric properties of the Modified-Modified Schober Test (MMST). DESIGN: This study compared range of motion measurements of lumbar flexion in low back pain (LBP) patients using the MMST with measurements calculated on X-rays as the gold standard, and compared the measurements taken by two independent examiners. METHOD: This study was conducted at the main hospital in the Outaouais area, Quebéc, Canada. Thirty-one subjects with LBP from private and public clinics participated in the study. After a warm-up session, measurements with the MMST were taken in neutral position and an X-ray technician took an exposure in the same position. RESULTS: Pearson's correlation test (r) between measurements made with the MMST and the gold standard, intra-class correlation coefficient (ICC), minimum metrically detectable change (MMDC) and confidence interval (CI) were used to analyze the data. The MMST demonstrated moderate validity (r=0.67; 95%CI 0.44-0.84), excellent reliability (intra: ICC=0.95; 95%CI 0.89-0.97; inter: ICC=0.91; 95%CI 0.83-0.96) and a MMDC of 1 cm. CONCLUSIONS: In our sample of LBP patients, the MMST showed moderate validity but excellent reliability and MMDC.
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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 | 0.009 | 0.028 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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; 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".