Variability in Cerebral Palsy Diagnosis
Bibliographic record
Abstract
BACKGROUND: Cerebral palsy (CP) is the most common childhood motor disability. The emergence of genetic CP etiologies, variable inclusion of hypotonic CP in international registries, and involvement of different medical disciplines in CP diagnosis can promote diagnostic variability. This variability could adversely affect patients' understanding of their symptoms and access to care. Therefore, we sought to determine the presence and extent of practice variability in CP diagnosis. METHODS: We surveyed physicians in the United States and Canada interested in CP on the basis of membership in the American Academy of Cerebral Palsy and Developmental Medicine or the Child Neurology Society Neonatal Neurology, Movement Disorders, or Neurodevelopmental Disabilities Special Interest Groups. The survey included the 2007 consensus definition of CP and 4 hypothetical case scenarios. RESULTS: Of 695 contacted physicians, 330 (47%) completed the survey. Two scenarios yielded consensus: (1) nonprogressive spastic diplegia after premature birth with periventricular leukomalacia on brain MRI (96% would diagnose CP) and (2) progressive spastic diplegia (92% would not diagnose CP). Scenarios featuring genetic etiologies or hypotonia as the cause of nonprogressive motor disability yielded variability: only 46% to 67% of practitioners would diagnose CP in these settings. CONCLUSIONS: There is practice variability in whether a child with a nonprogressive motor disability due to a genetic etiology or generalized hypotonia will be diagnosed with CP. This variability occurred despite anchoring questions with the 2007 consensus definition of CP. On the basis of these results, we have suggested ways to reduce diagnostic variability, including clarification of the consensus definition.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.038 | 0.198 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".