Knowledge and management of Adolescent Idiopathic Scoliosis among family physicians, pediatricians, chiropractors and physiotherapists in Québec, Canada: An exploratory study.
Bibliographic record
Abstract
BACKGROUND: Health professionals (HPs) are likely to encounter adolescent idiopathic scoliosis (AIS) patients. Best practice dictates that early detection leads to better decision making regarding optimal management. The aim of our study was to appraise the basic knowledge, evaluation and management skills concerning AIS care among family physicians, pediatricians, chiropractors, and physiotherapists. METHODS: A semi-structured questionnaire including 3 clinical scenarios was developed. Telephone interviews were conducted with 51 HPs to assess their knowledge of the clinical signs, risk factors, and management options of AIS and their preferences in clinical guidelines for AIS care. RESULTS: The majority of HPs (70-90%) would refer the patient who required prompt referral, but only 38-60% actually rated the case as requiring prompt referral. Forty percent of HPs (predominantly physiotherapists and family physicians) stated that they would not be comfortable providing AIS patient follow-up. Access to specialized care was considered a problem, and nearly all believed that establishment of clinical guidelines would be beneficial. CONCLUSIONS: Considerable gaps exist regarding the knowledge of the clinical signs and risk factors of AIS. The importance of a patient in need of a prompt referral is recognized by the majority of the HPs, but they believe that there are problems regarding accessibility to a specialist. Interprofessional collaboration is discussed as a promising approach to improve the management of AIS.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".