Bibliographic record
Abstract
Recent advances in modern perinatal and neonatal intensive care have led to an increase in the survival of premature infants. This increased survival, unfortunately, has not been accompanied by an improvement in neurodevelopmental outcomes. Premature infants, especially those with an extremely low birth weight (less than 1000 g) or those born at less than 28 weeks' gestation, are at increased risk of major disabilities and complex, 'low severity' dysfunctions that have significant, lasting effects on their school function, academic performance and behaviour, as well as on family function. Neonatal follow-up programs provide a number of functions to centres providing neonatal intensive care, including quality assurance and audits, research and follow-up clinical care to neonatal intensive care unit survivors and their families. The challenge for neonatal follow-up programs is to meet the often competing objectives of providing clinical services to children and their families while providing quality assurance and audits, and high-quality long-term outcome research components, given the available resources. There is also a need for ongoing research to develop and evaluate effective postdischarge intervention programs to improve the long-term outcome of prematurity and other neonatal complications. Developmental paediatricians - with their background and training in the provision of specialized health care to children and their care-givers with respect to developmental and psychosocial well-being, and in conducting developmental and behavioural disabilities research - play a valuable role in the follow-up assessment and care of neonatal intensive care unit graduates, and strengthen the multidisciplinary research groups necessary to assess long-term outcomes and the effects of perinatal and postdischarge interventions.
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 | 0.003 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.037 | 0.006 |
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".