Trying to be an Early BIRD: An exploration of factors impacting British Columbia’s intervention referral and diagnosis of cerebral palsy
Bibliographic record
Abstract
Objectives: To identify the average age of cerebral palsy (CP) diagnosis and referral for intervention services in British Columbia (BC) and explore key factors that may impact these outcomes. Methods: This study is a retrospective analysis of the Canadian CP Registry in BC between 2012 and 2021 (n = 187). Chart review recovered additional data on the ages of diagnosis and referral for intervention. The influence of clinical and demographic variables on the two outcomes were explored: Gross Motor Function Classification System (GMFCS) level, presence of non-motor disability, hallmark risk factors for CP, and ethnicity. Results: The mean age of CP diagnosis in the cohort was 25 months (standard deviation [SD]: 18), and the mean age of referral for intervention services was 3.8 months (SD: 4.6). A child at GMFCS level V was, on average, diagnosed 25.6 months earlier than a child with GMFCS level I (confidence interval [CI]: -39.625, -11.588, P = 0.001). GMFCS was not found to have a similarly high level of association with the age of referral for intervention. Ethnicity and the presence of non-motor disability did not have notable associations for either outcome. Children with hallmark risk factors were referred 7.5 months earlier than those without (CI: -11.4, -3.61, P < 0.005). Conclusions: GMFCS level is the most significant predictor of an early or late CP diagnosis. This may encourage increased education and resource efforts being placed towards early diagnosis of children with lower GMFCS levels. This project hopes to act as a starting point for further research efforts into facilitating early diagnosis within BC and Canada.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 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".