Development of the circumduction metric for identification of cervical motion impairment
Bibliographic record
Abstract
INTRODUCTION: Chronic neck pain results in considerable personal, clinical, and societal burden. It consistently ranks among the top three pain-related reasons for seeking healthcare. Despite its prevalence, neck pain is difficult to both assess and treat. Quantitative approaches are required since diagnostic imaging techniques rarely provide information on movement-related neck pain, and most common clinical assessment tools are limited to single plane motion measurement. METHODS: In this study, the ability of an inertial measurement unit to document the cervical motion characteristics of 28 people with chronic neck pain and 23 healthy controls was assessed. A total of six circumduction metrics and one neck circumduction trajectory model were proposed as identification metrics. RESULTS: Five metrics demonstrated significant differences between the two groups. The neck circumduction trajectory model successfully distinguished between the two groups. DISCUSSION: The evaluation of the proposed metrics provides proof of concept that novel metrics can be captured with relative ease in the clinical setting using an inexpensive wearable sensor headband. The derivation of the proposed model may open new lines of inquiry into the clinical utility of assessing the multiplanar movement of cervical circumduction. The results obtained from this study also provide additional insight for the development of a sensitive, quantifiable and real-world neck evaluation strategies.
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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 | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".