Neurodevelopmental Outcome of Extremely Low Birth Weight Infants from the Vermont Oxford Network: 1998–2003
Bibliographic record
Abstract
BACKGROUND: Physicians and parents face significant uncertainties when making care decisions for extremely low birth weight (ELBW) infants. Many published estimates of death and developmental outcome are from well-funded university programs and may not reflect outcomes of infants from a variety of settings. The best estimates of the probabilities of death and severe disability combine local experience and published data. OBJECTIVE: To describe the neurodevelopmental outcome of ELBW infants from centers of the ELBW Infant Follow-Up Group of the Vermont Oxford Network (VON) and to identify characteristics associated with severe disability. METHODS: Predefined measures of living situation, health and developmental outcome were collected at 18-24 months' corrected age for infants born from July 1, 1998 to December 31, 2003 with birth weights of 401-1,000 g at 33 North American VON centers. Logistic regression was used to identify characteristics associated with severe disability. RESULTS: 6,198 ELBW infants were born and survived until hospital discharge; by the time of follow-up, 88 infants (1.4%) had died. Of the remaining 6,110 infants, 3,567 (58.4%) were evaluated. Severe disability occurred in 34% of the assessed infants. Multivariate logistic regression suggested cystic periventricular leukomalacia, congenital malformation and severe intraventricular hemorrhage were the characteristics most highly associated with severe disability. There were marked variations among the follow-up clinics in the attrition rate. CONCLUSION: ELBW infants completing evaluation were at a high risk for severe disability. There are considerable differences among participating centers in attrition at follow-up. Further resources will be needed to study the effect of follow-up care for this group of infants.
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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 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".