Cerebral palsy diagnosis and the impact on hip surveillance enrollment
Bibliographic record
Abstract
AIM: To investigate the diagnosis at enrollment in the Child Health British Columbia Hip Surveillance Program for Children with Cerebral Palsy (CP) and review the etiologies of children enrolled without a CP diagnosis. METHOD: Data from 959 children (543 males, 416 females; mean [SD] age at enrollment 6 years 8 months [4 years 2 months]) enrolled in the program between September 2015 and December 2019 were retrospectively reviewed. Enrollment diagnosis, Gross Motor Function Classification System level, migration percentage, and age at enrollment were included. Chart reviews were completed to confirm diagnoses for all children. Etiologies were compared to a list of conditions that are included and excluded from CP registries. RESULTS: Diagnosis at enrollment was CP for 612 (64%), possible CP for 120 (13%), and 'other' for 220 (23%). No diagnosis was provided for seven (<1%). CP was confirmed for 700 (73%), including 106 (11.1%) enrolled as 'possible CP' or 'other'; 56 (5.8%) did not have CP due to progressive conditions. Migration percentage was similar across all groups at enrollment. INTERPRETATION: One in four children were enrolled in hip surveillance without a diagnosis of CP or possible CP. Encouraging participation in hip surveillance when children meet the clinical criteria for CP but do not have a confirmed CP diagnosis can improve access to care.
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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 | 0.003 | 0.020 |
| 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 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".