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Record W1986506609 · doi:10.1002/pd.2174

Information and decision‐making process for selective termination of dichorionic pregnancies: some French obstetricians' points of view

2008· article· en· W1986506609 on OpenAlexaff
Claire‐Marie Legendre, Christian Hervé, Michèle Goussot‐Souchet, Chantal Bouffard, Grégoire Moutel

Bibliographic record

VenuePrenatal Diagnosis · 2008
Typearticle
Languageen
FieldMedicine
TopicReproductive Health and Technologies
Canadian institutionsUniversité de Sherbrooke
Fundersnot available
KeywordsObstetricsProcess (computing)MedicineMEDLINEGynecologyComputer sciencePolitical science

Abstract

fetched live from OpenAlex

BACKGROUND: In France, neither Bioethics Law nor law related to abortion make reference to selective terminations (ST). Because they apply in the context of multiple pregnancies, ST raises problems which differ from those we usually see in prenatal medicine.We wanted to know: 1) which approaches were used by obstetricians to inform couples about processes and risks of ST, 2) their role in the decision-making process of couples, and 3) their representations about the level of autonomy that couples are able to assume. METHODS: Qualitative research, eight semi-structured interviews performed with eight obstetricians from seven public hospitals in Parisian region. RESULTS: Similarities: *Necessity to devote a lot of time to information. *Importance to give the couples the maximum of time for reflection. *Belief that the final decision belongs to couples. Discordances: *Heterogeneity of revealed information. *Discrepancy in the will to assure a complete and non directive information transfer. *Divergence in representations of what is an ethical support. *Differences in the limits of the autonomy of couples. CONCLUSIONS: All physicians believe that they respect the autonomy of couples, arguing that final decision belongs to them. Paradoxically, some results are indicative of a sizeable level of directiveness from the physicians.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.005
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.706
Threshold uncertainty score0.555

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.005
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.023
GPT teacher head0.316
Teacher spread0.292 · 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 teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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

Citations14
Published2008
Admission routes1
Has abstractyes

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