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Record W4412487456 · doi:10.1111/bioe.70021

Considering the Developing Entity in an Artificial Womb as a Patient

2025· article· en· W4412487456 on OpenAlexaff
Frédérique Drouin, Alice Cavolo, Vardit Ravitsky, Charles Dupras

Bibliographic record

VenueBioethics · 2025
Typearticle
Languageen
FieldMedicine
TopicEthics and Legal Issues in Pediatric Healthcare
Canadian institutionsUniversité de Montréal
Fundersnot available
KeywordsBeneficenceContext (archaeology)Developing countryIntervention (counseling)MedicinePsychologyLawNursingPolitical scienceAutonomy

Abstract

fetched live from OpenAlex

Artificial womb (AW) prototypes are currently being developed with the aim of improving the medical care of extremely premature infants. Despite the seemingly imminent reality of partial ectogenesis (i.e., gestation partially outside a human womb), there is persisting debate about the moral status of the fetus transferred in an AW-henceforth, the "developing entity." For some, AWs are simply another neonatal intensive cares' technology. Thus, developing entities in AWs should deserve the same protections as newborns in incubators. Others consider that AWs are fundamentally different technologies than incubators. Therefore, they believe that developing entities in AWs are new moral entities. These differences in perception generate disagreement about how developing entities in AWs should be treated and how decisions about them should be made. We argue that developing entities in AWs should be considered patients by transposing Chervenak and McCullough's "The fetus as a patient" proposition to the context of partial ectogenesis. As pregnant persons will have to consent to transfer their fetuses in AWs, and this technology will ultimately present itself as a beneficial medical intervention for viable developing entities in AWs, these latter would be patients, even if they are not legally and morally recognized as person. Thus, the moral obligations of beneficence and non-maleficence owed by physicians to their patients would apply to entities in AWs, ethically guiding their treatment and decision-making toward them.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.009
metaresearch head score (Gemma)0.011
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Commentary · Consensus signal: Commentary
Teacher disagreement score0.016
Threshold uncertainty score0.048

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0090.011
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0000.000
Science and technology studies0.0050.049
Scholarly communication0.0070.011
Open science0.0020.006
Research integrity0.0160.015
Insufficient payload (model declined to judge)0.0020.000

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.

Opus teacher head0.145
GPT teacher head0.451
Teacher spread0.307 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designTheoretical or conceptual
Domainnot available
GenreCommentary

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".

Quick stats

Citations0
Published2025
Admission routes1
Has abstractyes

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