Bibliographic record
Abstract
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 machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.005 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".