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Risks, Values, and Decision Making Surrounding Pregnancy

2007· review· en· W1984873231 on OpenAlexaff
Anne Drapkin Lyerly, Lisa M. Mitchell, Elizabeth Armstrong, Lisa H. Harris, Rebecca Kukla, Miriam Kuppermann, Margaret Olivia Little

Bibliographic record

VenueObstetrics and Gynecology · 2007
Typereview
Languageen
FieldMedicine
TopicEthics and Legal Issues in Pediatric Healthcare
Canadian institutionsCarleton UniversityUniversity of Victoria
Fundersnot available
KeywordsMedicineContext (archaeology)Psychological interventionPregnancyHealth careIsolation (microbiology)Risk perceptionPerceptionFamily medicineActuarial scienceIntensive care medicineNursingPsychologyBioinformatics

Abstract

fetched live from OpenAlex

Assessing, communicating, and managing risk are among the most challenging tasks in the practice of medicine and are particularly difficult in the context of pregnancy. We analyze common scenarios in medical decision making around pregnancy, from reproductive health policy and clinical care to research protections. We describe three tendencies in these scenarios: 1) to consider the probabilities of undesirable outcomes alone, in isolation from women's values and social contexts, as determinative of individual clinical decisions and health policy; 2) to regard any risk to the fetus, including incremental risks that would in other contexts be regarded as acceptable, as trumping considerations that may be substantially more important to the wellbeing of the pregnant woman; and 3) to focus on the risks associated with undertaking medical interventions during pregnancy to the exclusion of demonstrable risks to both woman and fetus of failing to intervene. These tendencies in the perception, communication, and management of risk can lead to care that is neither evidence-based nor patient-centered, often to the detriment of both women and infants.

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)0.001
metaresearch head score (Gemma)0.015
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Meta-epidemiology (narrow), Research integrity
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.982
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.015
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0020.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0020.002
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 head0.162
GPT teacher head0.469
Teacher spread0.306 · 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.

Study designNot applicable
Domainnot available
GenreReview

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

Citations114
Published2007
Admission routes1
Has abstractyes

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