School Difficulties at Adolescence in a Regional Cohort of Children Who Were Extremely Low Birth Weight
Bibliographic record
Abstract
OBJECTIVES: To compare measures of psychometric assessment and school difficulties in a cohort of extremely low birth weight (ELBW) teenagers and term controls, and to determine whether there is stability in psychometric measures between age 8 and the teen years. STUDY DESIGN: Longitudinal follow-up; geographically defined region. PARTICIPANTS: 150 of 169 (89%) ELBW survivors born between 1977 and 1982 and 124 of 145 (86%) sociodemographically matched term controls between 12 and 16 years of age. Psychometric measures: Wechsler Intelligence Scale for Children-Revised, Wide Range Achievement Test-Revised, and a validated parent questionnaire. RESULTS: Neurosensory impairments were present in 28% of ELBW and 1% of controls. The mean Wechsler Intelligence Scale for Children-Revised scores were ELBW: 89 +/- 19 and controls: 102 +/- 13. ELBW children did less well on Wide Range Achievement Test-Revised Reading, Spelling, and Arithmetic measures with mean scores in the range from 75 to 85. ELBW children <750 g were more disadvantaged, compared with those >/=750 g. A significantly higher proportion of ELBW children were receiving special educational assistance and/or had repeated a grade (ELBW: 58%; controls: 13%; odds ratio: 9.0). Paired analysis of within-cohort data at age 8 and teen years showed that for both cohorts Arithmetic scores declined, but there were small improvements in other measures, predominantly in the term children. CONCLUSIONS: Differences of 13 to 18 points in psychometric measures in ELBW teens compared with controls are both statistically significant and clinically relevant. Decreasing birth weight was associated with increased risk on all measures. The high utilization of special educational resources has economic implications, and the incremental cost attributable to being extremely premature needs to be determined.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 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 | 0.000 | 0.001 |
| 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".