10‐year evaluation of the use of medical abortion through telemedicine: a retrospective cohort study
Bibliographic record
Abstract
OBJECTIVE: To provide a descriptive overview and evaluate changes in the use and outcome of abortions provided worldwide by telemedicine in the past 10 years. DESIGN: Retrospective cohort study. SETTING: Multi-country. POPULATION/SAMPLE: 30 344 women who completed the follow-up survey of the telemedical abortion service Women on Web from January 2009 till January 2020. METHODS: Analyses of follow-up surveys, binary logistic regressions to test the association between year and outcomes. MAIN OUTCOME MEASURES: Rate of complete abortions, surgical interventions, ongoing pregnancies, blood transfusions per year, socio-economic situation, knowledge on medical abortion, acceptability of receiving service, appropriateness of method and the likelihood of recommending the service to a friend. RESULTS: Medical abortions were provided to 81 683 women, of whom 30 344 (37.2%) completed the follow-up survey. In total, 26 076 women reported doing the medical abortion, of whom 1.5% reported an ongoing pregnancy, 10.2% a surgical intervention and 0.6% a blood transfusion. Acceptability of the service was 99%, and 59.2% of the users reported previous knowledge of medical abortion. We found a significant increase in complete abortions in 2019 (odds ratio 1.92; 95% CI 1.59-2.31) and decrease in surgical interventions (odds ratio 0.49; 95% CI 0.40-0.60) compared with 2009. CONCLUSION: Low follow-up rates present a limitation in analysing trends in telemedical abortion usage. However, our findings suggest that it is a highly acceptable method around the world and that there has been an increase in complete abortions by telemedical abortions and a decrease in surgical interventions in the last 10 years. TWEETABLE ABSTRACT: In the last 10 years, there has been an increase in complete abortions and decrease in surgical interventions of telemedical abortion.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.036 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 0.000 |
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.
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 teacher head, 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".