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Record W2167695303 · doi:10.2522/ptj.20110371

A Survey of the McKenzie Classification System in the Extremities: Prevalence of Mechanical Syndromes and Preferred Loading Strategies

2012· article· en· W2167695303 on OpenAlexaff
Stephen May, Richard Rosedale

Bibliographic record

VenuePhysical Therapy · 2012
Typearticle
Languageen
FieldMedicine
TopicBone fractures and treatments
Canadian institutionsLondon Health Sciences Centre
Fundersnot available
KeywordsMedicineObservational studyCategorizationPhysical therapyMedical diagnosisTest (biology)Prospective cohort studyMEDLINEPhysical medicine and rehabilitationSurgeryPathologyArtificial intelligence

Abstract

fetched live from OpenAlex

BACKGROUND: Classification of patients with extremity problems is commonly based on patho-anatomical diagnoses, but problems exist regarding reliability and validity of the tests and diagnostic criteria used. Alternatively, a classification system based on patient response to repeated loading strategies can be used to classify and direct management. OBJECTIVE: The purpose of this study was to investigate the prevalence of McKenzie's classification categories among patients with extremity problems and the loading strategies used in their management. DESIGN: This was a prospective, observational study. METHODS: Thirty therapists among 138 invited (response rate=22%) with a Diploma in Mechanical Diagnosis and Therapy (MDT) were identified from the McKenzie Institute International registry and recruited worldwide to complete an e-mailed questionnaire. They provided data about their age, years qualified, years since gaining a diploma, and practice, and prospectively provided data on anatomical site and categorization for 15 consecutive patients with extremity problems. RESULTS: Data were gathered on 388 patients; classification categories were as follows: derangement (37%); contractile dysfunction (17%); articular dysfunction (10%); and "other" (36%), of which 20% were postsurgery or posttrauma. Exercise management strategies and syndrome application varied considerably among anatomical sites. Classification categories remained consistent in 85.8% of patients over the treatment episode. LIMITATIONS: These findings are not generalizable to therapists who are not experienced with use of MDT in the extremities. CONCLUSIONS: This study demonstrates that trained clinicians can classify patients with extremity problems into MDT classifications and that these classifications remain stable during the treatment episode. Further work is needed to test the efficacy of this system compared with other approaches, but if derangements are as common as this survey suggests, the findings have important prognostic implications because this syndrome is defined by its rapid response to repeated movements.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.157
Threshold uncertainty score0.151

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.103
GPT teacher head0.334
Teacher spread0.231 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations33
Published2012
Admission routes1
Has abstractyes

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