Developing a province-wide hip surveillance program for children with cerebral palsy: from evidence to consensus to program implementation: a mini-review
Bibliographic record
Abstract
Hip displacement is a common orthopedic problem in children with cerebral palsy (CP) that can result in significant morbidity. Hip surveillance has been shown to reduce the incidence of hip dislocations in children with CP and to reduce the need for salvage hip surgeries. Guidelines for hip surveillance have been developed and can be adapted to meet local needs. Implementation of surveillance guidelines for a population of children is complex and highly dependent upon the region, province/state, or country's system of care for children with CP. Recognizing that implementation of the evidence on hip surveillance was necessary in British Columbia, a Canadian province spanning 1 million square kilometers, a comprehensive, coordinated approach to hip surveillance was developed collaboratively by provincial stakeholders. Surveillance guidelines and a desired implementation plan were established based on the best available research evidence, current international practice, and service delivery in British Columbia. Staged implementation preceded full provincial roll out. Implementation was supported by detailed communication, knowledge translation, and evaluation plans. This province-wide hip surveillance program is the first of its kind in North America.
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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 | 0.009 | 0.027 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.004 |
| Bibliometrics | 0.007 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".