Smart Brace versus Standard Rigid Brace for the Treatment of Scoliosis: A Pilot Study
Bibliographic record
Abstract
The outcomes of brace treatment for scoliosis depend on how the brace is used. Simply prescribing a brace does not mean it will be worn properly. A smart brace has been developed to control the brace wear tightness with the expectation that appropriately worn braces will improve outcomes. Twelve brace candidates (10F; 2M) agreed to participate into this study and were randomly divided into 2 groups. The smart brace group used the smart brace for the first year, and then wore the standard brace for the following year. The standard rigid brace group wore their TLSO for 2 years. Both groups were followed for 3 years after they finished the brace treatment. The smart brace group showed better quality of brace wear, wearing their brace at the prescribed tightness level a higher proportion of time than the standard brace group. All subjects in the smart brace group had successful outcomes, Cobb angle changed less than 5°, whereas 2/6 subjects in the standard brace group had unsuccessful bracing. One had 7° increment and 1 underwent surgery. The smart brace group also reported that the smart brace was more comfortable to wear than the standard rigid brace.
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 | 0.003 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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".