Bibliographic record
Abstract
The study by Wang et al1 in this issue of Pediatrics highlights the lack of quality indicators for the neurodevelopmental follow-up of very low birth weight (VLBW) survivors. It is an excellent example of the effort required to develop explicit process criteria to evaluate the quality of VLBW follow-up care. The authors systematically reviewed 437 articles on clinical predictors, screening, and treatment for VLBW children by searching the PubMed database, screening articles by prominent authors in the field and articles cited in the Agency for Healthcare Research and Quality evidence report Criteria for Determining Disability in Infants and Children: Low Birth Weight ,2 and reviewing the clinical practice guidelines for follow-up care of VLBW infants. They drafted a set of 96 potential quality-of-care indicators in 5 content areas (general care; physical health; vision, hearing, speech, and language; developmental behavioral assessment; and psychosocial assessment), which a panel of 10 experts rated for validity, feasibility, and quality of evidence. Validity and feasibility were rated on a 9-point scale (1, low; 9, high). The quality of evidence was rated by using the Canadian Task Force on Periodic Health Examination system,3 which ranks quality of evidence from data generated from randomized, controlled trials as level I, data from cohort or case-control studies and nonrandomized, controlled trials … Address correspondence to Linda L. Wright, MD, Center for Research for Mothers and Children, National Institute of Child Health and Human Development, National Institutes of Health, Building 6100, Room 4B05J, Bethesda, MD 20815. E-mail: wrightl{at}mail.nih.gov
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 | 0.031 | 0.207 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.009 | 0.013 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.011 | 0.009 |
| Insufficient payload (model declined to judge) | 0.011 | 0.002 |
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".