Bibliographic record
Abstract
Our objective was to determine the clinical spectrum of pediatric hemiparesis by identifying the relative frequency of various diagnoses and comorbid conditions seen in these children. Case records of all patients with hemiparesis in a single practice over an 11-year period were reviewed with reference to clinical features, etiologic determination, and comorbid conditions. Ninety-two children were identified: 73 (79.3%) had a congenital hemiparesis and 19 (20.7%) had an acquired hemiparesis. An abnormal perinatal history (P = .003), prematurity (P = .016), and younger age at onset of symptoms (P < .001) were associated with a congenital hemiparesis. The overall etiologic yield was 83.7% (82.2% in the congenital and 89.5% in the acquired). The top four etiologic entities were cerebrovascular ischemia (40.2%), periventricular leukomalacia (18.5%), intracranial hemorrhage (16.3%), and cerebral dysgenesis (13%). Factors predictive of establishing an underlying etiology included birth prior to 34 weeks' gestation (P = .034), global developmental delay (P = .048), epilepsy (P = .024), and having appropriate imaging modalities (P = .001). Half of these children had a concurrent global developmental delay, associated epilepsy (odds ratio 3.67; 95% confidence interval 1.40-9.72), and prematurity (odds ratio 5.41; 95% confidence interval 1.56-18.80). A third of these children developed epilepsy. Multivariate predictive factors for epilepsy included global developmental delay (odds ratio 4.20; 95% confidence interval 1.44-12.27), cerebrovascular ischemia (odds ratio 5.10; 95% confidence interval 1.76-14.77), and term birth (odds ratio 3.87; 95% confidence interval 1.20-12.56). The majority of children with hemiparesis have a congenital etiology. The diagnostic yield is higher than previously reported; however, specific underlying etiologies need to be better determined. Comorbid conditions of global developmental delay and epilepsy have a high prevalence in this population, contributing to overall morbidity.
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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 | 0.000 | 0.003 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".