Clinical characteristics influencing timing of cerebral palsy diagnosis in neonatal follow-up
Bibliographic record
Abstract
Objectives: To describe clinical characteristics influencing the timing of cerebral palsy (CP) diagnosis in a traditional neonatal follow-up clinic (NFC) setting. Methods: Retrospective observational cohort study involving preterm infants, born <29 weeks gestation and/or birthweight <1000 g between January 2005 and December 2014, with CP and followed in Calgary's NFC. Infant data were collected, including demographics, perinatal and neonatal parameters, cranial ultrasound (cUS) results, co-occurring conditions, and CP characteristics (timing of suspicion and diagnosis, type, topography, distribution, and Gross Motor Function Classification System [GMFCS] level). This cohort was divided into two groups, early (diagnosed <19 months corrected age [CA]) and late (diagnosed ≥19 months CA), based on the median age of CP diagnosis, and characteristics were compared. Results: A total of 99 infants met the inclusion criteria. Median age at first CP suspicion was 9 months CA (interquartile range [IQR] 14) and median age at diagnosis was 19 months CA (IQR 17), with median time lag from suspicion to diagnosis of 6 months (IQR 12). CP characteristics associated with diagnosis at an earlier age included higher GMFCS level, mixed type (compared to spastic only), and upper and lower extremities involvement. Infant characteristics, severity of cUS results, and co-occurring conditions were not different between early and late groups. Conclusions: CP diagnosis timing is affected by GMFCS level, motor type, and distribution. Especially in infants with CP involving less motor impairment, there is a prolonged delay between CP suspicion and formal diagnosis. This gap may be amenable to quality improvement initiatives aimed at targeted implementation of early assessment tools.
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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 | 0.001 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| 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".