Getting pregnant after tubal sterilization: surgical reversal or IVF?
Bibliographic record
Abstract
BACKGROUND: When women regret having had a tubal sterilization, is the pregnancy rate higher with surgical reversal or IVF? METHODS: This retrospective cohort study analyses the delivery rates of 163 patients undergoing IVF treatment (n = 79) or surgical reversal (n = 84). Pregnancy outcomes were obtained by reviewing medical records or contacting private physicians and patients. The life table method was used to calculate the chance of becoming pregnant and to construct cumulative pregnancy curves. Cumulative pregnancy curves are compared by log rank tests. A P-value of <0.05 is considered as statistically significant. The cost-effectiveness of the two strategies was also evaluated. RESULTS: Patient characteristics did not differ between the two groups. The cumulative delivery rate during 72 months was 52.0% in the IVF group and 59.5% in the reversal group (ns). Age was the only factor that influenced delivery rates significantly. The cumulative delivery rate for patients aged < 37 years was 52.4% after IVF and 72.2% after reversal (P = 0.012), while cumulative delivery rates for patients aged 37 years or older were, respectively, 51.4 and 36.6%, a difference that did not reach statistical significance. The average cost per delivery was 11,707 euros for IVF, compared with 6015 euros for surgical reversal. However, in patients aged 37 years or older the difference in cost was smaller. CONCLUSION: Considering the cumulative delivery rates involved, surgical reversal is recommended for patients younger than 37; older patients are advised to opt for IVF.
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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".