Hemiparetic cerebral palsy: clinical pattern and imaging in prediction of outcome.
Bibliographic record
Abstract
BACKGROUND: Hemiparetic cerebral palsy (HCP) is described as having two main forms: arm-dominant, associated with large cortical/subcortical lesions; leg-dominant, associated with lesions of central white matter. Epilepsy and cognitive deficits are common in the former pattern and rare in the latter. Some authors have recommended routine imaging studies in children with HCP as an assessment of etiology and a predictor of outcome. The present study compares the relative values of clinical analysis and imaging in predicting epilepsy and cognitive disabilities. METHODS: Forty-one consecutive patients with HCP underwent careful clinical assessment and imaging studies (primarily computed tomography) and were followed prospectively for the development of recurrent afebrile seizures and academic difficulties. RESULTS: Twenty of the 41 patients (48.8%) were arm-dominant, 14/41 (34.1%) leg-dominant, and 7/41 (17.1%) proportional (arm = leg). The principal imaging findings were: arm-dominant patients - large arterial infarcts, porencephalic cysts, brain malformations; leg-dominant - periventricular leukomalacia; proportional - porencephaly. Arm-dominant hemiparesis and radiologic evidence of cortical pathology were both predictive of cognitive deficits (odds ratios 14.2 [95% CI 2.6, 75.8] and 5.7 [95% CI 1.4. 22.3] respectively). For the development of epilepsy, both evaluation techniques were again predictive, with imaging findings of cortical pathology being particularly powerful (clinical pattern OR 18.0 [95% CI 3.0, 107.7]; imaging OR 80.7 [95% CI 8.5, 767.3]). CONCLUSIONS: In this study, the clinical pattern of HCP and the radiological findings were both predictive of outcome, with absence of cortical pathology on imaging being particularly predictive for the absence of epilepsy. While the clinical pattern, in isolation, appears helpful in predicting outcome, our results suggest that both evaluation tools have important roles to play in the evaluation of HCP patients.
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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 | 0.002 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".