Brace Wear Characteristics during the first 6 months for the Treatment of Scoliosis
Bibliographic record
Abstract
Bracing is the most commonly used non-surgical treatment for adolescent idiopathic scoliosis (AIS) and requires an extensive commitment on the part of the patient and family. However, demonstrating efficacy of brace treatment for AIS has been hampered by the lack of compressive information about wear characteristics. The first 6 months is considered a critical time to evaluate whether AIS patients will commit to the treatment and may predict the treatment outcome. The characteristics of brace wear can assist clinicians to provide better support and aid long term compliance with treatment. This study describes the first 6 month brace wear characteristics in 15 AIS patients (12F;3M) who were prescribed full-time brace wear. There was a statistically significant increase in wear time (p = 0.02) after brace fitting and the brace wear tightness stabilized after month 4. The force at the major pressure pad area continuously decreased after month 2. A moderate correlation was found between the (quantity * quality) of the brace wear at month 6 and the change of Cobb angle (first out of brace - pre brace) (r2 = 0.47). The more time that the brace was worn and the higher proportion of time worn at the prescribed tightness or above improves the likelihood of a better treatment result.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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".