Wireless, Soft, Low-Profile Bioelectronics for Quantitative Diagnostics of Cervical Dystonia
Bibliographic record
Abstract
Cervical dystonia (CD) is one of the most chronic neurological syndromes, leading patients to repetitive movements and abnormal postures. CD patients suffer substantial decrease in function and quality of life including normal activities of daily living such as cooking, dressing, eating, and driving. The traditional methods for assessment of CD have involved clinical rating scales that is the Toronto Western Spasmodic Torticollis Rating-2 (TWSTRS-2) [1]. However, rating these scales based on visual observation is subject to change upon the level of training and expertise of the clinician, which significantly prevents rapid and accurate identification of CD. Here, we introduce the first demonstration of a soft skin-like, wireless electronic system, referred to as “SKINTRONICS”, which can quantify 4 types of disorder (torticollis, laterocollis, retrocollis, and anterocollis) and the severity levels of symptoms. The ideas begin with the employment of a highly sensitive accelerometer for accurate recording of total head movements in a soft, ultrathin, conformal wearable platform. The soft-membrane construction allows the biopatch gently and seamlessly mounted on the forehead of a patient with negligible effects on natural head motions while supplying sufficient and reliable digital signals throughout the diagnostic process of CD. To validate the device accuracy, a direct comparison between SKINTRONICS-based assessment and TWSTRS-2 based manual assessment is provided from a clinical study with 5 patients and 5 healthy subjects. Collective results indicate the new class of technology platforms for wireless, diverse, accurate monitoring of abnormal head movements illuminates more effective and safe treatments to patients for improved CD healthcare. Reference [1] C. L. Comella et al., Mov. Disord. 2016, 31(4), 563-569.
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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 | 0.000 | 0.001 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| 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".