Severe retinopathy of prematurity predicts delayed white matter maturation and poorer neurodevelopment
Bibliographic record
Abstract
OBJECTIVE: To determine whether severe retinopathy of prematurity (ROP) is associated with (1) abnormal white matter maturation and (2) neurodevelopmental outcomes at 18 months' corrected age (CA) compared with neonates without severe ROP. DESIGN: We conducted a prospective longitudinal cohort of extremely preterm neonates born 24-28 weeks' gestational age recruited between 2006 and 2013 with brain MRIs obtained both early in life and at term-equivalent age. Severe ROP was defined as ROP treated with retinal laser photocoagulation. Using diffusion tensor imaging and tract-based spatial statistics (TBSS), white matter maturation was assessed by mean fractional anisotropy (FA) in seven predefined regions of interest. Neurodevelopmental outcomes were assessed with Bayley Scales of Infant and Toddler Development-III (Bayley-III) composite scores at 18 months' CA. Subjects were compared using Fisher's exact, Kruskal-Wallis and generalised estimating equations. SETTING: Families were recruited from the neonatal intensive care unit at BC Women's Hospital. PATIENTS: Of 98 extremely preterm neonates (median: 26.0 weeks) assessed locally for ROP, 19 (19%) had severe ROP and 83 (85%) were assessed at 18 months' CA. RESULTS: Severe ROP was associated with lower FA in the posterior white matter, and with decreased measures of brain maturation in the optic radiations, posterior limb of the internal capsule (PLIC) and external capsule on TBSS. Bayley-III cognitive and motor scores were lower in infants with severe ROP. CONCLUSIONS: Severe ROP is associated with maturational delay in the optic radiations, PLIC, external capsule and posterior white matter, housing the primary visual and motor pathways, and is associated with poorer cognitive and motor outcomes at 18 months' CA.
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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 | 0.001 | 0.003 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".