Development of a Pressure Control System for Brace Treatment of Scoliosis
Bibliographic record
Abstract
Bracing is a common nonsurgical treatment for scoliosis, but its effectiveness has been debated. Some clinical studies have shown efficacy of brace treatment is correlated to how the brace has been worn. The more often the patients wear their braces to the prescribed tightness as well as the prescribed length of wear each day, the better the treatment outcome. A system of four wireless pressure control devices was developed to understand brace wear-time and regulate a target pressure range at the brace-body interface. Each pressure control device could function independently and be embedded in the brace at key pressure areas. Such a system could improve the quality of brace wear-making the treatment more effective and refining our understanding of the three-pressure-point brace treatment concept during daily activities. This paper reports the system development and validation. The system was tested on four healthy subjects for 2 h without pressure regulation and 2 h with regulation. The results show that the pressure regulation doubled the time spent in a desired pressure range on average (from 31% to 62%). Brace-wear time was logged correctly. The system was also validated through a seven-day continuous test, and a fully charged battery could run for 30 days without requiring recharge.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".