Online Calculator to Improve Counseling of Short-Term Neonatal Morbidity and Mortality Outcomes at Extremely Low Gestational Age (23–28 Weeks)
Bibliographic record
Abstract
Objective Extremely low gestational age (ELGA) infants are at high risk of perinatal and neonatal morbidity and mortality. Accurate and relevant data are essential for developing a health care plan and providing realistic estimates of infants' outcomes. Study Design Retrospective analysis of all infants delivered between 23(0/7) and 28(6/7) weeks' gestation over 11 years at a single center. Using logistic regression analysis, gestational age (GA)-specific mortality and morbidity rates, and the effects of gender, antenatal corticosteroids, multiple gestation, and birth weight (BW) were determined. Results Of the 766 study infants, 644 (84.1%) were admitted to the neonatal intensive care unit, of which 502 (75.8%) survived to discharge. GA, antenatal corticosteroids, and BW were significant predictors of survival (GA: odds ratio [OR] = 1.83, 95% confidence interval [CI] = 1.64-2.04; corticosteroids: OR = 7.62, 95% CI = 5.19-11.18; BW: OR = 1.56, 95% CI = 1.44-1.69). Increasing BW correlated with a decreasing mortality rate. Conclusion This study provides recent outcome data of ELGA infants delivered at a tertiary level center. The results have been translated into an online counseling tool (http://murmuring-brook-6600.herokuapp.com/ELGA.html).
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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 | 0.002 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.027 | 0.007 |
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".