Investigating Pediatricians’ Practice, Knowledge, and Barriers in Diagnosing Cerebral Palsy
Bibliographic record
Abstract
Background/Objectives: Data from the Canadian Cerebral Palsy (CP) Registry suggests that children in British Columbia (BC) are diagnosed, on average, at 25 months of age. This is much later than currently recommended. This study aimed to examine current practices and beliefs of pediatricians in the province related to CP and CP diagnosis. Methods: All pediatricians and subspecialty pediatricians in the province were invited to participate in two consecutive online surveys. The initial survey aimed to assess current practice, knowledge of CP, and beliefs about diagnosis. The second survey, which was distributed to the same group of pediatricians, as well as pediatric neurologists and geneticists, aimed to re-assess current practice and identify specific barriers and facilitators to CP diagnosis. Results: The two surveys were completed by 76 and 59 respondents, respectively. Less than 60% of general pediatricians, in both surveys, reported diagnosing children with CP. In survey 2, only 50% of respondents felt that pediatricians should provide a diagnosis of CP. Most general pediatricians (93%) identified that pediatricians, with support from a developmental pediatrician or neurologist, should provide a diagnosis. Common barriers to an early CP diagnosis included uncertainty about other potential diagnoses and uncertainty over diagnosing at a young age. Lack of access to education and therapists to help inform the diagnosis were also frequently identified barriers. Conclusions: While general pediatricians are knowledgeable about CP, a significant proportion in those surveyed were not diagnosing CP, despite believing that early diagnosis is important. Findings from these surveys have identified that general pediatricians have gaps in knowledge, skills, and confidence in diagnosing CP. Support from a developmental pediatrician or neurology colleague was identified as a potential strategy to support earlier diagnosis.
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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 | 0.006 | 0.025 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".