A compliance real-time monitoring system for the management of the brace usage in adolescent idiopathic scoliosis patients: a pilot study
Bibliographic record
Abstract
BACKGROUND: Patient compliance is essential to the effectiveness of brace treatment for adolescent idiopathic scoliosis (AIS) patients. Subjective measurements like questionnaires and inquiries proved to be arguably inaccurate. Although some scholars have applied temperature/force monitors to measuring patient compliance objectively, few studies to date could monitor patient compliance in real time. The objective of this study is to describe and evaluate a compliance real-time monitoring system of the brace usage in AIS patients. METHODS: A compliance real-time monitoring system (specifically consisting of a compliance monitor, a WeChat Mini Program, a cloud-based storage system and a website backstage management system) was designed to manage the brace treatment. Thirty patients receiving brace treatment were enrolled. They were told to upload the data at least once a day. Clinicians downloaded the compliance data and communicated with the patients online based on their analysis of data at least once every 3 months. The measured force, quality compliance (measured force / baseline force), measured time, and quantity compliance (measured time/ prescribed time) were used to evaluate patient compliance. Patients were also asked to rate their satisfaction at the final follow-up. RESULTS: Twenty-eight patients were included in the final analysis. The mean baseline force was 1.23 ± 0.28 N. The mean measured force was 0.79 ± 0.29 N. The mean quality compliance was 64.8 ± 22.2%. The prescribed time of all patients was 23 h. The mean measured time was 14.1 ± 2.9 h. The mean quantity compliance was 61.3 ± 12.6%. Both the quality and quantity compliance during the first 3 months of treatment was significantly lower than the latter 3 months. In this study, 96.4% (27/28) patients were satisfied with the use of the monitoring system, among whom 21.4% (6/28) are very satisfied and 75.0% (21/28) are somewhat satisfied. CONCLUSIONS: The compliance real-time monitoring system, without evaluating the clinical and radiographic outcomes for now, has already shown some feasibility and effectiveness for the management of the brace usage in AIS patients. This system, as a useful tool for online patient management and patient-clinician communication, would be potentially employed on a large scale in future for clinicians to improve the compliance and satisfaction of AIS patients who have received brace treatment.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".