Prediction of brace treatment outcomes by monitoring brace usage.
Bibliographic record
Abstract
Brace treatment is the most commonly used non-surgical treatment method for adolescent idiopathic scoliosis (AIS). This study determined whether curve progression can be predicted by how often and how well children with AIS wear their braces. Twenty subjects (3M, 17F) who were diagnosed with AIS and had worn their braces from six months up to 1 year participated into this study. All subjects were prescribed Boston style braces and have now completed their brace treatment. On average, the brace was used 57% of the prescribed time. Peterson's risk of progression (Risser sign, age, apex of curve and imbalance of curve) predicted only 3-8% of the curve progression of brace subjects. Knowing how brace subjects used their braces in terms of brace tightness increases the prediction rate to 12-21%; and wear time further increase it to 25-36%. Adding the multiple of brace tightness and wear time improves curve progression prediction to 41-54%. To be most effective, the brace should be worn as prescribed in both tightness and time manners.
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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 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 | 0.000 | 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".