Development of a Bedside Tool to Predict the Diagnosis of Cerebral Palsy in Term-Born Neonates
Bibliographic record
Abstract
Importance: Cerebral palsy (CP) is the most common abnormality of motor development and causes lifelong impairment. Early diagnosis and therapy can improve outcomes, but early identification of infants at risk remains challenging. Objective: To develop a CP prognostic tool that can be applied to all term neonates to identify those at increased risk of developing CP. Design, Setting, and Participants: This case-control study used data from the Canadian Cerebral Palsy Registry (data collected from January 2003 to December 2019) for children with CP and the Alberta Pregnancy Outcomes and Nutrition study (mothers enrolled from May 2009 to September 2012; data extracted in 2020) for controls. There were 2771 children with CP and 2131 controls evaluated; 941 and 144, respectively, were removed for gestational age less than 37 weeks at birth, 565 with CP removed for incomplete data, and 2 controls removed for a diagnosis of CP. Data were analyzed from April to August 2022. Exposures: Potential risk factors were selected a priori based on the literature, including maternal, intrapartum, and infant characteristics. Main Outcomes and Measures: Diagnosis of CP, defined as a disorder of motor function due to a nonprogressive brain abnormality before age 1 year and classified by Gross Motor Function Classification System levels I to V. Results: Of 3250 included individuals, 1752 (53.9%) were male, and the median (IQR) gestational age at birth was 39 (38-40) weeks. Encephalopathy was present in 335 of 1184 infants with CP (28%) and 0 controls. The final prediction model included 12 variables and correctly classified 75% of infants, with a sensitivity of 56% (95% CI, 52-60) and specificity of 82% (95% CI, 81-84). The C statistic was 0.74 (95% CI, 71-76). Risk factors were found to be additive. A proposed threshold for screening is probability greater than 0.3, with a sensitivity of 65% (95% CI, 61-68) and specificity of 71% (95% CI, 69-73). The prognostic tool identified 2.4-fold more children with CP than would have presented with encephalopathy (odds ratio, 13.8; 95% CI, 8.87-22.65; P < .001). Conclusions and Relevance: In this case-control study, a prognostic model using 12 clinical variables improved the prediction of CP compared with clinical presentation with encephalopathy. This tool can be applied to all term newborns to help select infants for closer surveillance or further diagnostic tests, which could improve outcomes through early intervention.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".