Importance of the delivery‐to‐insertion interval in immediate postpartum intrauterine device insertion: A secondary analysis
Bibliographic record
Abstract
OBJECTIVE: To evaluate the delivery-to-insertion interval for copper postpartum intrauterine devices (PPIUDs). METHODS: Secondary analysis of two related studies at five academic sites in India from March 2015 to July 2016. IUDs were inserted within 48 hours of vaginal delivery. Women (n=560) were grouped by whether they underwent postplacental (≤10 minutes) or immediate (>10 minutes) insertion. Outcomes were complete expulsion at the 6-8-week follow-up (primary), and IUD-to-fundus distance, as assessed by postinsertion ultrasound (secondary). RESULTS: Overall, 93 (16.6%) women received a postplacental PPIUD and 467 (83.4%) received an immediate PPIUD. Complete expulsion at follow-up was 3.2% (n=3) in the postplacental and 7.5% (n=35) in the immediate postpartum group (P=0.176; difference in proportions, 4.3%; 95% confidence interval, -2.0 to 8.1). Distance from the fundus did not differ between the two groups (P=0.107); high fundal placement (≤10 mm from the internal endometrial verge) was achieved for most women. CONCLUSION: The present data challenge previous guidance on the timing of PPIUD insertion. The 10-minute insertion window is a barrier to uptake and should be reassessed for inclusion in service delivery guidelines. A flexible interval would accommodate the multiple post-delivery tasks of providers and increase access to PPIUD.
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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 | 0.009 | 0.041 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".