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Is there a benefit of low-dose aspirin in assisted reproduction?

2006· review· en· W2017784663 on OpenAlexaff
Salim Daya

Bibliographic record

VenueCurrent Opinion in Obstetrics & Gynecology · 2006
Typereview
Languageen
FieldMedicine
TopicOvarian function and disorders
Canadian institutionsOttawa Fertility Centre
Fundersnot available
KeywordsMedicineAspirinLow dose aspirinPregnancyMiscarriageRandomized controlled trialObstetricsReproductionInfertilityIn vitro fertilisationPlaceboClinical trialPregnancy rateGynecologyInternal medicineAlternative medicinePathology

Abstract

fetched live from OpenAlex

PURPOSE OF REVIEW: Assisted reproduction is an effective treatment for infertile women but, despite advances in ovarian stimulation and laboratory techniques, overall pregnancy rates are still relatively low suggesting that methods to improve implantation are required. One strategy is to increase the blood flow to the uterus with low-dose aspirin. The objective of this review is to determine if low-dose aspirin improves clinical pregnancy rates when administered to infertile women undergoing treatment with assisted reproduction. RECENT FINDINGS: A retrospective review was unable to demonstrate improved pregnancy rates when low-dose aspirin was compared with no treatment. Such studies, however, have limited value in clinical decision-making because of poor methodological quality. A recent high-quality randomized, placebo-controlled trial of low-dose aspirin was also unable to demonstrate any benefit, a finding supported by a meta-analysis of 10 trials that collectively had sufficient power to detect a clinically relevant improvement in clinical pregnancy rate. Evidence also exists that low-dose aspirin is potentially harmful, because of increased bleeding problems, miscarriage and congenital anomalies. SUMMARY: Given the lack of efficacy and the potential for harmful effects to both the patient and her offspring, low-dose aspirin should not be administered to infertile women undergoing treatment with assisted reproduction.

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)0.004
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.963
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0020.004
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0010.001
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 head0.113
GPT teacher head0.394
Teacher spread0.281 · 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

Citations8
Published2006
Admission routes1
Has abstractyes

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