Inter-rater reliability of the McKenzie System of Mechanical Diagnosis and Therapy in the examination of the knee
Bibliographic record
Abstract
OBJECTIVE: The McKenzie System of Mechanical Diagnosis and Therapy (MDT) is a widely used method of classification and management of musculoskeletal problems. Although MDT has been investigated for its reliability and efficacy in the management of spinal pain, few studies have evaluated the system when applying it to musculoskeletal problems in the extremities, in particular the knee. The purpose of this study was to investigate the inter-rater reliability of MDT when classifying clinical vignettes describing patients with musculoskeletal knee pain. METHODS: This study was divided into two phases. First, 10 clinicians experienced in the use of MDT were recruited to write a total of 60 clinical vignettes based upon the initial assessment of their past patients with knee pain. Second, six different MDT raters were recruited to rate 53 selected vignettes and reliability was determined using Fleiss Kappa. RESULTS: = 0.72). There was no statistically significant difference between therapists with different levels of training. DISCUSSION: MDT demonstrated acceptable reliability among trained raters to classify clinical vignettes describing patients with musculoskeletal knee pain. To generalize the use of the system to more users, future research should continue to investigate the reliability of MDT using raters with lower levels of training and experience and assess reliability in real patients. LEVEL OF EVIDENCE: 5.
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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 | 0.076 | 0.134 |
| 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 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".