Ethics ain't easy: do we need simple rules for complicated ethical decisions?
Bibliographic record
Abstract
BACKGROUND: Recommendations from national bodies regarding extremely preterm infants have focussed almost exclusively on thresholds for intervention based upon estimated gestational age (GA) alone. METHODS: We reviewed policy statements that address active intervention for newborn infants and compare them with those that are available for older patients. We reviewed research, examining attitudes towards preterm infants, uncertainties in GA assessment and other factors important in determining prognosis at the time of birth. RESULTS: Policy statements regarding active care of very preterm infants treat this population differently from others in morally significant ways--without rationalizing this discrepancy. Extremely preterm infants are devalued in medical and lay opinion compared to older individuals with similar outcomes. Uncertainty in GA estimates often covers a range with vastly differing prognoses. Sex, birth weight, inborn-outborn status and use of antenatal steroids are vitally important in prognosis, but clinical findings in the delivery room are not. Most policy statements fail to account for these factors. CONCLUSION: Simplistic policies based on GA alone should be avoided. Decision making for extremely preterm infants should recognize that they are each unique and must be individualized, taking into account all relevant prognostic factors and the values and wishes of the families.
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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 | 0.176 | 0.301 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.011 | 0.081 |
| Scholarly communication | 0.021 | 0.045 |
| Open science | 0.006 | 0.010 |
| Research integrity | 0.022 | 0.049 |
| Insufficient payload (model declined to judge) | 0.006 | 0.004 |
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".