No test medical abortion – a review of the evidence on selective use of preabortion testing
Bibliographic record
Abstract
PURPOSE OF REVIEW: The last decade has seen a cascade of different telemedicine models for medical abortion (MA) being tested and implemented. Among these service delivery models is the 'no-test' MA model, in which care is provided remotely and eligibility for the MA is based on history alone. The purpose of this review is to provide an overview of the existing evidence for no-test MA. RECENT FINDINGS: The evidence base for no-test MA relies heavily on cohort and noncomparative studies predominantly from high resource settings. Recent findings indicate that no-test MA is safe, effective, and highly acceptable. Diagnoses of ectopic pregnancy and underestimation of gestational age were rare. Identified advantages included shortening time to access MA and mitigating access barriers such as cost, and geographical barriers. Abortion seekers valued omitting the ultrasound citing reasons such as privacy concerns, costs, more flexibility, and control. The impacts of no-test MA on unscheduled postabortion contacts and visits and on contraceptive use were unclear due to limited evidence. SUMMARY: No-test MA can be provided to complement other care pathways including those with some or no in-person care. Further research is needed to allow for widespread adoption of no-test MA and scale-up in a variety of contexts, including low-resource settings.
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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 | 0.005 | 0.028 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.003 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".