Abstract TP172: Clinical And Neuroimaging Predictors Of Dyskinetic Cerebral Palsy
Bibliographic record
Abstract
Background: Dyskinetic cerebral palsy (DCP) is a non-progressive disorder that results from lesions to the developing fetal brain. Neuroimaging patterns and risk factors for DCP in pre-term infants are poorly understood. Furthermore, neuroimaging differences between preterm and term infants with DCP and its relationship to clinical outcomes are not well established. Objectives: 1) To describe neuroimaging differences between term and preterm infants with DCP. 2) To investigate relationships between neuroimaging patterns and clinical motor outcome of infants with DCP. Methods: Patients with DCP were identified through The Cerebral Palsy Network where clinical details and magnetic resonance images (MRI) were collected on children with DCP. To determine lesion volume and location, manual segmentation was performed using ITK-SNAP software by study neuroradiologist. Lesion severity was graded using a semi-quantitative scale for structural MRIs(Laporta-Hoyos et al. 2018) based on the number of lobes and subcortical structures involved. Motor outcomes was assessed with the Gross Motor Function Classification System. Results: Twenty-nine patients with DCP were identified. Preterm infants (n=12, [male= 8, female=4]), and term infants (n=16 term, [male= 6, female=10]) differed in mean gestational age (30.5 versus 39.1 weeks respectively, p=.02). The frequency of periventricular leukomalacia in preterm (89%) and term-born (58%) infants differed (p = 0.02). Lesion severity was associated with: the ventral posterior lateral (VPL) thalamus (r=0.574,p =.01), the posterior limb of the internal capsule (PLIC) (r=0.437p = .04) in term infants, and the right (r = 0.504, p = .01) and left putamen r = 0.629, p = .002) in term infants. Significant negative correlations were found between lesion severity and gross motor function in the VPL thalamus (r= -0.624, p = .001), PLIC (r= -0.735, p < .001), right (r= -0.50, p= .016) and left putamen (r= -0.54 p = .008). Conclusion: Our results suggest the timing of lesions in term-born and preterm infants with DCP are associated with neuroimaging lesion patterns. Involvement of subcortical structures and lesion severity scores were associated with gross motor function in this cohort of patients with DCP.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".