Neurologic Examination Findings Associated With Small Cerebellar Volumes After Prematurity
Bibliographic record
Abstract
To help clinicians understand what to expect from small cerebellar volumes after prematurity, this study aims to characterize the specific impacts of small cerebellar volumes on the infant neurologic examination. A prospective cohort of preterm newborns (<32 weeks’ gestational age) had brain magnetic resonance imaging (MRI) studies at term-equivalent age. Cerebellar volumes were compared with neurologic examination findings in follow-up, adjusting for severity of intraventricular hemorrhage, white matter injury, and cerebellar hemorrhage. Deformation-based analyses delineated regional morphometric differences in the cerebellum associated with these findings. Of 119 infants with MRI scans, 109 (92%) had follow-up at 19.0±1.7 months corrected age. Smaller cerebellar volume at term was associated with increased odds of truncal hypotonia, postural instability on standing, and patellar hyperreflexia ( P < .03). Small cerebellar volume defined as <19 cm 3 by 40 weeks was associated with 7.5-fold increased odds of truncal hypotonia ( P < .001), 8.9-fold odds postural instability ( P < .001), and 9.7-fold odds of patellar hyperreflexia ( P < .001). Voxel-based deformation-based morphometry showed postural instability associated with paravermian regions. Small cerebellar volume is associated with specific abnormalities on neurologic examination by 18 months of age, including truncal tone, reflexes, and postural stability.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".