Postnatal Prediction of Gestational Age Using Newborn Fetal Hemoglobin Levels
Bibliographic record
Abstract
INTRODUCTION: In many parts of the developing world procurement of antenatal gestational age estimates is not possible, challenging provision of appropriate perinatal care. This study aimed to develop a model for postnatal gestational age estimation utilizing measures of the newborn hemoglobin levels and other metabolic analyte data derived from newborn blood spot samples. METHODS: We conducted a retrospective cohort analysis of 159,215 infants born January 2012-December 2014 in Ontario, Canada. Multivariable linear and logistic regression analyses were used to evaluate the precision of developed models. RESULTS: Models derived from a combination of hemoglobin ratios and birthweight were more precise at predicting gestational age (RMSE1·23weeks) than models limited to birthweight (RMSE1·34). Models including birthweight, hemoglobin, TSH and 17-OHP levels were able to accurately estimate gestational age to ±2weeks in 95·3% of the cohort and discriminate ≤34 versus >34 (c-statistic, 0·98). This model also performed well in small for gestational age infants (c-statistic, 0·998). DISCUSSION: The development of a point-of-care mechanism to allow widespread implementation of postnatal gestational age prediction tools that make use of hemoglobin or non-mass spectromietry-derived metabolites could serve areas where antenatal gestational age dating is not routinely available.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".