Relationship Between Attrition and Neurodevelopmental Impairment Rates in Extremely Preterm Infants at 18 to 24 Months
Bibliographic record
Abstract
OBJECTIVE: To assess the effect of loss to follow-up rates at 18 to 24 months on neurodevelopmental outcome statistics for infants of less than 1000 g birth weight or less than 28 weeks' gestational age. DATA SOURCES: MEDLINE, EMBASE, PubMed, and Cochrane Library databases (January 1, 2000, to June 30, 2010). STUDY SELECTION: We searched for studies reporting outcomes of infants of less than 1000 g birth weight or less than 28 weeks' gestational age who were born after 1990. MAIN EXPOSURE: Eligible articles had to report the primary outcome and follow-up rates at 18 to 24 months. MAIN OUTCOME: Our primary composite outcome of neurodevelopmental impairment (NDI) was any of a mental developmental quotient 2 SDs below the mean, using the Bayley Scales of Infant Development II; cerebral palsy; visual impairment; or significant hearing impairment. RESULTS: Of 43 publications describing outcomes at 18 to 24 months, 20 provided rates of follow-up, describing a total of 34,185 infants. The NDI rates ranged between 12.4% and 57.5%. Follow-up rates ranged between 71.6% and 100%. Higher rates of NDI were significantly correlated with greater loss to follow-up (r(2) = 0.38, P = .007). Higher rates of both NDI and loss to follow-up were seen in the United States compared with Canada, the United Kingdom, Finland, Denmark, Austria, Germany, and Australia (r(2) = 0.70, P = .001). CONCLUSIONS: Ascertainment bias may overestimate NDI in extremely low-birth-weight or extremely low-gestational-age survivors at 18 to 24 months. Alternatively, the characteristics of different populations and health systems may contribute to higher rates of attrition and higher rates of NDI.
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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 | 0.028 | 0.155 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.005 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".