Brace Prescription Patterns in Patients Referred to Orthopaedic Clinics for Adolescent Idiopathic Scoliosis (AIS)
Bibliographic record
Abstract
Even if braces for scoliosis are broadly used, there are no data on the orthopaedic medical practice to evaluate the circumstances of brace prescription. This study aims at comparing scoliosis brace prescription patterns with generally recognized standards. A cross-sectional study was carried out in 2006-2007 on all confirmed AIS patients referred to a paediatric scoliosis clinic for a first visit. Agreement between the actual brace prescription patterns and standards for immediate prescription was analyzed, following the recommendations of the Quebec Scoliosis Network (QSN), as well as the Scoliosis Research Society (SRS) therapeutic inclusion criteria. In addition, chi-2 tests and logistic regression models were used to identify variables related to brace prescription. Amongst the 321 AIS patients, immediate brace treatment was recommended in 70 cases, for about 50% of concordance with the defined criteria. Variables describing the patients' maturity (age, Risser, onset of menses) and deformity magnitude (Cobb angle and rib hump), as well as the treating physician, were the main determinants of brace prescription. Despite the professional consensus on immediate bracing norms, under and over-prescription of brace were documented in this study. Better understanding of these patterns would require documentation of motives associated with prescription at the individual level.
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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".