Ethical dilemmas in the care of pregnant women: rethinking “maternal–fetal conflicts”
Bibliographic record
Abstract
Ms. A is 19 years old and 25 weeks pregnant. Although her pregnancy was unplanned, at no time has she considered pregnancy termination. During a prenatal office visit, Ms. A reveals that she has a daily drug habit that includes crack cocaine and intravenous narcotics. She refuses to consider a change in her behavior, despite a thorough review of the potential effects of her substance abuse on her pregnancy outcome. Specifically, she refuses to participate in a methadone or other substance-abuse program. Ms. B is 24 years old and has been in labor for 18 hours. The cervical dilatation has not progressed past 3 cm. The fetal heart rate tracing has been worrisome but is now seriously abnormal, showing a profound bradycardia of 65 beats per minute. This bradycardia does not resolve with conservative measures. Repeat pelvic examination reveals no prolapsed cord and confirms a vertex presentation at 3 cm dilatation. The obstetrician explains to Ms. B that a cesarean section will be necessary because of suspected fetal distress. Ms. B absolutely refuses, saying “No surgery.” What are ethical dilemmas in the care of pregnant women? When a pregnant woman engages in behavior(s) that may be harmful to her fetus, or refuses a recommended diagnostic or therapeutic intervention aimed at enhancing fetal health and well-being, her physician may experience an ethical dilemma.
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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 | 0.008 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.005 | 0.027 |
| Scholarly communication | 0.006 | 0.008 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.007 | 0.018 |
| Insufficient payload (model declined to judge) | 0.003 | 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".