Inter-rater Reliability of the McKenzie Method of Mechanical Diagnosis and Therapy for the Provisional Classification of Low Back Pain in Adolescents and Young Adults
Bibliographic record
Abstract
OBJECTIVE: To investigate the inter-rater reliability of Mechanical Diagnosis and Therapy (MDT)-trained Diplomats in classifying adolescents and young adults with lumbar pain. METHODS: Forty-three participants (mean age 15 ± 2 years) with lumbar pain, with or without lower extremity symptoms, were assessed simultaneously by three MDT Diploma holders and classified into one of three groups: 1) Derangement, 2) Dysfunction, 3) Postural/OTHER. Inter-rater reliability was calculated using the Fleiss kappa statistics with 95% confidence intervals (CI). Analyses were repeated with the younger (11 to 15 years old) and older (16 to 21 years old) age groups. RESULTS: There was moderate reliability (Fleiss kappa = 0.50, 95% CI = 0.45 to 0.54) for the entire sample, which was statistically significant (p < 0.001). There was good reliability in older participants (Fleiss kappa = 0.63, 95% CI = 0.57 to 0.70), but poor reliability in younger participants (Fleiss kappa = 0.33, 95% CI = 0.27 to 0.39). There was 100% agreement in classifications among assessors for 70% of participants. DISCUSSION: The MDT system has moderate reliability when classifying lumbar pain in adolescents and young adults. Future reliability studies may include a balanced group for classifications or a second session.
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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 | 0.040 | 0.065 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 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".