Risk factors and consequences of undiagnosed cesarean scar pregnancy: a cohort study in China
Bibliographic record
Abstract
BACKGROUND: The historically high cesarean section rate and the recent change in second-child policy could increase the risk of cesarean scar pregnancy (CSP) in China. This study aims to assess risk factors and consequences of undiagnosed CSP in China. METHODS: We conducted a retrospective cohort study between January 2013 and December 2017 in Qingyuan, Guangdong, China. Independent risk factors for undiagnosed CSP at the first contact with healthcare providers were assessed by log binomial regression analysis. Occurrence of serious complications was compared between undiagnosed and diagnosed CSP cases. RESULTS: A total of 195 women with CSP were included in the analysis. Of them, 81 (41.5%) women were undiagnosed at the first contact with healthcare providers. Women initially cared in primary or secondary hospitals were at increased risk for undiagnosed CSP: adjusted relative risks (95% confidence intervals) were 3.28 (2.06, 5.22) and 1.91 (1.16, 3.13), respectively, compared with women initially cared in the tertiary hospital. Undiagnosed CSP cases had higher incidences in serious complications (11 versus 0) and post-surgery anemia (23 (28.4%) versus 8 (7.0%)), stayed longer in hospital, and cost higher than diagnosed CSP cases. CONCLUSIONS: Initial care provided at primary or secondary maternity care facilities is an important risk factor for undiagnosed CSP, with serious consequences to the affected women.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".