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Record W4281565440 · doi:10.2217/fon-2021-1255

Pregnancy and Perinatal Outcomes Following Exposure to Antineoplastic Agents Around Pregnancy within the US FDA Adverse Event Reporting System

2022· article· en· W4281565440 on OpenAlexaff
Omar Abdel‐Rahman, Sunita Ghosh

Bibliographic record

VenueFuture Oncology · 2022
Typearticle
Languageen
FieldMedicine
TopicCancer Risks and Factors
Canadian institutionsUniversity of Alberta
FundersIpsenAmgen
KeywordsMedicineAdverse Event Reporting SystemPregnancyAbortionAdverse effectObstetricsCongenital malformationsPharmacology

Abstract

fetched live from OpenAlex

Objective: To review pregnancy and perinatal outcomes associated with exposure to antineoplastic drugs around pregnancy as reported within the US FDA Adverse Event Reporting System (FAERS). Methods: The FAERS database was accessed and reports of exposure to antineoplastic drugs before/during pregnancy 2000–2020 were reviewed. An analysis of the frequency of different adverse pregnancy outcomes and perinatal outcomes was conducted for all agents as well as for specific categories of antineoplastic agents. Results: A total of 5312 reports of pregnancy exposure to antineoplastic drugs within the FAERS database were found to be eligible and were included in the current study. The most frequent adverse pregnancy outcomes included premature delivery (21.8%) and abortion (11.9%). The most frequent adverse perinatal outcomes included congenital malformations (15.9%) and fetal/neonatal death (12.9%). Conclusions: Within the limitations of the study (especially the lack of an accurate denominator), premature delivery, abortion, fetal/neonatal death and congenital malformations seemed to be the main risks associated with pregnancy exposure to antineoplastic drugs.

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)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.047
Threshold uncertainty score0.624

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
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 head0.020
GPT teacher head0.315
Teacher spread0.295 · 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

Citations4
Published2022
Admission routes1
Has abstractyes

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