New Technologies to Dating Pregnancy at Birth
Bibliographic record
Abstract
The chapter provides a vision of new methods of pregnancy dating at birth to overcome the high costs of existing approaches or lack of access to the existing technologies. The authors have presented a literature review on recent scientific reports exploring novel technologies, such as those based on the newborn’s skin maturity assessment as machine learning models based on clinical data. The effortlessness of new approaches based on simplified clinical systems contrasting with molecular genetics and newborns screening analytes is discussed, even in scientific validation. Without the intention of an exhaustive or systematic review, we searched databases for reports concerning postnatal gestational age, prediction or estimate, novel approach, low and medium-income countries since 2015. Therefore, the authors did not compromise to offer a comprehensive picture of all postnatal gestational age methods. On the other hand, prematurity identification at birth remains a challenge in many birth settings, mainly in a scenario with scarce resources. Although postnatal pregnancy dating methods have strengths and disadvantages, this information is critical to recognize the risk of the newborn during the first hours of life, justifying technological investments.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.026 | 0.012 |
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".