Disabilities and health of extremely low‐birthweight teenagers: a population‐based study
Bibliographic record
Abstract
AIM: Evaluation of long-term outcome of extremely low-birthweight (ELBW) teenagers born in Iceland in 1991-1995. METHOD: Participants, 30 of 35 ELBW survivors and 30 full term control teenagers (14-19 years), were assessed for disabilities, health problems and learning difficulties. Results of national standardized tests in mathematics and Icelandic language were compared with results of neurodevelopmental assessment at 5 years of age. RESULTS: A quarter of the ELBW teenagers had disabilities. All were initially diagnosed with neurodevelopmental disorders early in life and neurosensory and/or intellectual disabilities were confirmed later in childhood. Chronic lung disorders, neurological problems and psychiatric disorders were most common health problems. Growth parameters were within normal limits for most of the ELBW teenagers. Learning difficulties affected 57% of the ELBW teenagers, 20% attended special education classes and 37% required special teaching. Results of national standardized tests were significantly lower for ELBW survivors and were significantly related to the results of neurodevelopmental assessment at 5 years of age. INTERPRETATION: A quarter of ELBW teenagers have disabilities albeit most of them mild. Chronic health problems and learning difficulties affect many ELBW survivors. Changes with time emphasize need of long-term follow-up.
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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 | 0.001 |
| 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 | 0.001 |
| Open science | 0.000 | 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".