Deciphering classification systems for neck pain—Understanding the content of classification systems to enhance physiotherapy management of neck pain
Bibliographic record
Abstract
Abstract Background Neck pain is a prevalent and disabling condition. Conservative management of this condition has shown only moderate effects. A solution to improve treatment effectiveness is to sub‐group patients into a classification system (CS) that allows for more personalised care. However, current stratification methods have only shown short‐term efficacy for pain. Given the limitations of these tools, it is pertinent to understand how these CSs are composed to be able to propose alternative patient management solutions. Objective To identify and examine the different components of classification systems specific to patients with neck‐related conditions. Method A systematic literature search was performed on 3 databases (PubMed, Scopus and CINAHL). Only systematic reviews, with or without meta‐analysis, and scoping reviews reporting CS with associated treatment for neck pain were included. Bias evaluation was performed through risk of bias in systematic review tools. Results From the search strategy, 741 citations were retrieved, and seven studies were included. From these studies, 37 CS with associated treatments were extracted. Mobilisations showed that 64% were constructed using physical findings, 61% of CS were guided by symptom modulation, 25% used results of self‐reported questionnaire, 14% used individual characteristics, 14% incorporated cognitive findings, 8% used neurological findings, 3% used results of medical diagnostic test, and 3% incorporated environmental findings. Fear‐avoidance beliefs was the only cognitive parameter considered among CS. Conclusion This study shows that existing classification systems for neck pain are limited and lack coverage of all potential drivers of pain and disability. The lack of recognition of psychosocial and pain neuroscience parameters may partly explain the limited effectiveness of these tools.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".