International expert recommendations of clinical features to prompt referral for diagnostic assessment of cerebral palsy
Bibliographic record
Abstract
AIM: To establish international expert recommendations on clinical features to prompt referral for diagnostic assessment of cerebral palsy (CP). METHOD: An online Delphi survey was conducted with international experts in early identification and intervention for children with CP, to validate the results obtained in two previous consensus groups with Canadian content experts and knowledge users. We sent two rounds of questionnaires by e-mail. Participants rated their agreement using a 4-point Likert scale, along with optional open-ended questions for additional feedback. Additionally, a panel of experts and knowledge-users reviewed the results of each round and determined the content of subsequent surveys. RESULTS: Overall, there was high-level of agreement on: (1) six clinical features that should prompt referral for diagnosis; (2) two 'warning sign' features that warrant monitoring rather than immediate referral for diagnosis; and (3) five referral recommendations to other healthcare professionals to occur simultaneously with referral for diagnosis. INTERPRETATION: There was high agreement among international experts, suggesting that the features and referral recommendations proposed for primary care physicians for early detection of CP were broadly generalizable. These results will inform the content of educational tools to improve the early detection of CP in the primary care context. WHAT THIS PAPER ADDS: International experts provide strong agreement on clinical features to detect cerebral palsy. Consensus on clinical 'warning signs' to monitor over time. Referral recommendations from primary care to specialized health services are identified.
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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 | 0.132 | 0.209 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.007 | 0.003 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.004 | 0.005 |
| Research integrity | 0.006 | 0.004 |
| Insufficient payload (model declined to judge) | 0.007 | 0.002 |
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".