Quantifying performance metrics of cervical spine mobilization for improved education and clinical outcomes: Early experience with a novel wearable device
Bibliographic record
Abstract
INTRODUCTION: Evaluation of manual skills training for spinal rehabilitation has been stagnant for 40 years. Observation and mimicry are coupled with feedback from "expert" observers to train and evaluate learners, relying on rater experience to discern speed and force. Spinal manipulation is a controlled act under the Regulated Health Professions Act (1991) as it is not without risk. The discordance between current methods for evaluating proficiency and the potential risks of inadequate application is a critical gap. METHODS: This work reports a novel wearable device that measures finger forces via microstrain bending of the physiotherapist's nail. The device leaves the tactile finger pad unobstructed and does not interfere with treatment application. Five expert-level physiotherapists performed a standard postero-anteriorly directed spinal segmental mobilization treatment for 1 min at 1-1.5 Hz rhythm. RESULTS: The device successfully measured all treatment forces (0.2-27.3 N). Physiotherapists applied a maximum force rate of 0.03 ± 0.01 N/s with a rhythm of 1.76 ± 0.38 Hz. In 15 trials, there were no device failures. The device was easily applied and removed, and physiotherapists were able to walk about and interact normally with patients. CONCLUSIONS: These results indicate that this technology can be integrated into a skills training program to provide quantitative feedback for objective assessments.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".