Extremely premature birth bioethical decision-making supported by dialogics and pragmatism
Bibliographic record
Abstract
Moral values in healthcare range widely between interest groups and are principally subjective. Disagreements diminish dialogue and marginalize alternative viewpoints. Extremely premature births exemplify how discord becomes unproductive when conflicts of interest, cultural misunderstanding, constrained evidence review, and peculiar hierarchy compete without the balance of objective standards of reason. Accepting uncertainty, distributing risk fairly, and humbly acknowledging therapeutic limits are honorable traits, not relativism, and especially crucial in our world of constrained resources. We think dialogics engender a mutual understanding that: i) transitions beliefs beyond bias, ii) moves conflict toward pragmatism (i.e., the truth of any position is verified by subsequent experience), and iii) recognizes value pluralism (i.e., human values are irreducibly diverse, conflicting, and ultimately incommensurable). This article provides a clear and useful Point-Counterpoint of extreme prematurity controversies, an objective neurodevelopmental outcomes table, and a dialogics exemplar to cultivate shared empathetic comprehension, not to create sides from which to choose. It is our goal to bridge the understanding gap within and between physicians and bioethicists. Dialogics accept competing relational interests as human nature, recognizing that ultimate solutions satisfactory to all are illusory, because every choice has downside. Nurturing a collective consciousness via dialogics and pragmatism is congenial to integrating objective evidence review and subjective moral-cultural sentiments, and is that rarest of ethical constructs, a means and an end.
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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 | 0.086 | 0.080 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.008 | 0.064 |
| Scholarly communication | 0.017 | 0.017 |
| Open science | 0.003 | 0.021 |
| Research integrity | 0.009 | 0.022 |
| 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".