Inter-examiner reliability of diplomats in the mechanical diagnosis and therapy system in assessing patients with shoulder pain
Bibliographic record
Abstract
OBJECTIVE: To investigate the inter-examiner reliability of Mechanical Diagnosis and Therapy (MDT)-trained diplomats in classifying patients with shoulder disorders. The MDT system has demonstrated acceptable reliability when used in patients with spinal disorders; however, little is known about its utility when used for appendicular conditions. METHODS: Fifty-four clinical scenarios were created by a group of 11 MDT diploma holders based on their clinical experience with patients with shoulder pain. The vignettes were made anonymous, and their clinical diagnoses sections were left blank. The vignettes were sent to a second group of six international McKenzie Institute diploma holders who were asked to classify each vignette according to the MDT categories for upper extremity. Inter-examiner agreement was evaluated with kappa statistics. RESULTS: There was 'very good' agreement among the six MDT diplomats for classifying the McKenzie syndromes in patients with shoulder pain (kappa = 0.90, SE = 0.018). The raw overall level of multi-rater agreement among the six clinicians in classifying the vignettes was 96%. After accounting for the actual MDT category for each vignette, kappa and the raw overall level of agreement decreased negligibly (0.89 and 95%, respectively). DISCUSSION: Using clinical vignettes, the McKenzie system of MDT has very good reliability in classifying patients with shoulder pain. As an alternative, future reliability studies could use real patients instead of written vignettes.
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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 | 0.036 | 0.082 |
| 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) | 0.002 | 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".