School-Age Outcomes in Children Who Were Extremely Low Birth Weight From Four International Population-Based Cohorts
Bibliographic record
Abstract
OBJECTIVE: The aim of this study was to determine whether learning and school problems in extremely low birth weight (ELBW) and reference children differ between cohorts in different countries. METHODS: Participants were 4 international population-based cohorts of ELBW survivors who were 500 to 1000 g birth weight from New Jersey, central-west Ontario, Bavaria, and Holland (n = 532) and were followed longitudinally from birth. Psychometric data were collected independently and prospectively and included at least 1 measure of cognitive status and 1 measure of achievement administered to the children between the ages of 8 and 11 years. Adjustments were made for comparison of all measures based on reference norms within each country. Information on special educational assistance and grade repetition was obtained from the parents. RESULTS: The overall follow-up rate was 84% (range: 74%-90%; n = 436). The proportion of children who performed within the normal range (> or =85) were as follows: IQ between 44% and 62%; reading between 46% and 81%; arithmetic between 31% and 76%; and spelling between 39% and 65%. Children from New Jersey had the lowest rates of cognitive and achievement deficits, and Bavarian children did less well in achievement scores relative to their peers and other cohorts. Despite these differences, more than half of all cohorts required special educational assistance and/or repeated a grade. CONCLUSIONS: School difficulties were found to be a serious sequelae of ELBW in all 4 countries, an observation that has social and economic implications.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 |
| 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 | 0.001 | 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".