Gestation-Based Viability–Difficult Decisions with Far-Reaching Consequences
Bibliographic record
Abstract
Most clinicians rely on outcome data based on completed weeks of gestational of fetal maturity for antenatal and postnatal counseling, especially for preterm infants born at the margins of viability. Contemporary estimation of gestational maturity, based on ultrasounds, relies on the use of first-trimester scans, which offer an accuracy of ±3-7 days, and depend on the timing of the scans and the measurements used in the calculations. Most published literature on the outcomes of babies born prematurely have reported on short- and long-term outcomes based on completed gestational weeks of fetal maturity at birth. These outcome data change significantly from one week to the next, especially around the margin of gestational viability. With a change in approach solely from decisions based on survival, to disability-free survival and long-term functional outcomes, the complexity of the parental and care provider's decision-making in the perinatal and postnatal period for babies born at less than 25 weeks gestation remains challenging. While sustaining life following birth at the margins of viability remains our priority-identifying and mitigating risks associated with extremely preterm birth begins in the perinatal period. The challenge of supporting the normal maturation of these babies postnatally has far-reaching consequences and depends on our ability to sustain life while optimizing growth, nutrition, and the repair of organs compromised by the consequences of preterm birth. This article aims to explore the ethical and medical complexities of contemporary decision-making in the perinatal and postnatal periods. We identify gaps in our current knowledge of this topic and suggest areas for future research, while offering a perspective for future collaborative decision-making and care for babies born at the margins of viability.
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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 | 0.044 | 0.115 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.021 |
| Scholarly communication | 0.009 | 0.008 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.005 | 0.011 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".