P.075 EEG features reflecting the neurodevelopmental assessment at term equivalent age in preterm born infants
Bibliographic record
Abstract
Background: In Canada, 7% of children are born preterm between 29 and 36 weeks gestational age (GA). Electroencephalography (EEG) provides a bedside evaluation of brain activity, yet the clinical significance of several EEG patterns requires further study. The goal of this study is to determine the EEG features at term equivalent age (TEA) that correlate with neurodevelopmental evaluation at TEA in infants born between 29-36 weeks GA. Methods: Prospective cohort study of preterm infants born 29-36 weeks GA with 1 hour EEG recording at TEA. EEG discontinuity index (proportion <25mcV amplitude) and spectral power densities were calculated as well as the mean and maximum values of interburst intervals. At TEA, neurodevelopment was evaluated using the General Movement Optimality Score (GMOS). Linear regression analyses were used to evaluate the association between EEG features and neurodevelopmental assessment. Results: Eighty-two children (median GA 33.6 weeks) were included (47 males). Median GMOS was 29.0 (IQR 24.3-35.0). A greater EEG discontinuity index was associated with reduced GMOS (B -6.85; 95% CI -12.13,-1.57; p=0.012). Conclusions: At TEA, a greater EEG discontinuity index was associated with a more abnormal neurodevelopmental assessment. Ongoing longitudinal neurodevelopmental assessments are needed to better evaluate the prognostic potential of TEA EEG.
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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) | 0.003 | 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".