Use of consensus methods to determine the early clinical signs of cerebral palsy
Bibliographic record
Abstract
OBJECTIVES: To develop expert-informed content regarding the early motor attributes of cerebral palsy (CP) that should prompt physician referral for diagnostic assessment of CP, as well as concurrent referral recommendations. This content will be used in the creation of knowledge translation (KT) tools for primary care practitioners and parents. METHODS: , using an integrated KT approach. RESULTS: Six attributes were identified that should prompt referral for diagnosis. If the child demonstrates: Early handedness <12 months; stiffness or tightness in the legs between 6 and 12 months; persistent fisting of the hands >4 months; persistent head-lag >4 months; inability to sit without support >9 months; any asymmetry in posture or movement. Five referral recommendations were agreed upon: Motor intervention specialist (physical therapy and/or occupational therapy) for ALL; speech-language pathology IF there is a communication delay; audiology IF there is parental or healthcare professional concern regarding a communication delay; functional vision specialist (e.g., optometrist or occupational therapist) IF there is a vision concern (e.g., not fixating, following, or tracking); feeding specialist (e.g., occupational therapist, speech-language pathologist) IF there are feeding difficulties (e.g., poor sucking, poor swallowing, choking, and/or not gaining weight). CONCLUSION: Rigorous consensus methods provided the initial evidence necessary to inform the content of tools to assist primary care providers in the early detection of CP. Results will be validated through a Delphi process with international experts, and user-friendly formats of this KT tool will be developed collaboratively with stakeholders.
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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 | 0.389 | 0.585 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.004 | 0.005 |
| Bibliometrics | 0.021 | 0.011 |
| Science and technology studies | 0.005 | 0.005 |
| Scholarly communication | 0.009 | 0.005 |
| Open science | 0.006 | 0.013 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.008 | 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".