Endometrial thickness as a predictive value of ectopic pregnancy in in-vitro fertilization/intracytoplasmic sperm injection cycles: a case-control study
Bibliographic record
Abstract
BACKGROUND: The incidence rate of ectopic pregnancy (EP) with assisted reproductive technology (ART) has been reported to be higher than that of spontaneous pregnancy. Endometrial thickness (EMT) is considered an independent risk factor. OBJECTIVES: Evaluation of endometrial thickness as a predictor of ectopic pregnancy in vitro fertilization/intracytoplasmic sperm injection (IVF/ICSI) cycles. DESIGN: Case-control study. SETTING: Women's Health Specialist Hospital, formerly known as King Abdulaziz Medical City, National Guard Health Affairs, Riyadh, Saudi Arabia. PATIENTS AND METHODS: This study was a 1:2 matched case-control study that enrolled 24 ectopic pregnancy patients and 50 matched intrauterine pregnancy patients. They were matched on maternal age, the presence of tubal factor, and parity. The estimated sample size for patients with ectopic pregnancy was 25-30, whereas that for controls was estimated to be 60 patients. The sampling technique was a purposive (nonprobability). MAIN OUTCOME MEASURES: Endometrial thickness after IVF/ICSI is a predictor of ectopic pregnancy. SAMPLE: 79 patients. RESULTS: Multivariate logistic regression analysis was performed to analyze EP predictors, and a receiver operating characteristic (ROC) curve was used to evaluate the predictors of EP. After adjustment for other factors in the logistic regression model, we found that the tubular factor increased the risk of EP by 7.6 times, whereas the ovarian factor significantly decreased EP by 85%. Other factors, including EMT, did not significantly affect the probability of developing ectopic pregnancy. CONCLUSIONS: EMT was not predictive nor protective of EP in women who underwent ART cycles. Nevertheless, the presence of tubular factors has strong predictive value, whereas ovarian factors have significant protective value against EP. LIMITATIONS: The study design affects the generalizability and level of evidence provided. The small sample size and single-center inclusion had an impact on the results and statistical tests used.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| 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".