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Record W2024462996 · doi:10.1155/2012/724591

Ethical Issues in Pharmacologic Research in Women Undergoing Pregnancy Termination: A Systemic Review and Survey of Researchers

2011· review· en· W2024462996 on OpenAlexafffund
Christelle Gedeon, Alejandro A. Nava‐Ocampo, Gideon Koren

Bibliographic record

VenueObstetrics and Gynecology International · 2011
Typereview
Languageen
FieldMedicine
TopicPregnancy and Medication Impact
Canadian institutionsHospital for Sick ChildrenUniversity of Toronto
FundersCanadian Institutes of Health ResearchHospital for Sick Children
KeywordsMedicineEthical issuesPregnancyIntensive care medicineFamily medicineObstetricsEngineering ethicsGenetics

Abstract

fetched live from OpenAlex

Objective. To evaluate the ethics of performing research in the field of maternal-fetal medicine involving women undergoing pregnancy termination. Methods. We identified published pharmacological studies performed during elective pregnancy termination. In addition, a questionnaire was administered to investigate whether this research would be acceptable to professionals performing research in the field of maternal-fetal pharmacology. Results. The majority of participants believe that this form of research is necessary to furthering our understanding of drug use in pregnancy. Twenty studies were identified in women undergoing a pregnancy termination where exogenous drug was administered and drug measurement conducted during an abortion. The majority of studies were completed by international groups and not in North America or Western Europe. Conclusions. While a majority of respondents to the survey felt that, although research in women undergoing a pregnancy termination is ethically acceptable, 40% stated that it is not likely to be approved by institutional review boards of most North American medical institutions.

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)0.119
metaresearch head score (Gemma)0.191
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Research integrity
Consensus categoriesnone
DomainCandidate signal: Methods · Consensus signal: none
Study designCandidate signal: Systematic review · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.998
Threshold uncertainty score0.629

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.1190.191
Meta-epidemiology (narrow)0.0000.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0060.006
Science and technology studies0.0020.002
Scholarly communication0.0030.003
Open science0.0010.003
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0010.000

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 head0.328
GPT teacher head0.520
Teacher spread0.193 · 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.

Study designSystematic review
DomainMethods
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

Citations7
Published2011
Admission routes2
Has abstractyes

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