Prognostic value of a serum β-hCG cut off, 12 days after fresh embryo transfer, on predicting live birth among Ugandan women
Bibliographic record
Abstract
Abstract Background: Human Chorionic Gonadotropin (hCG) is secreted by the embryo as early as the first week of life. Several studies have proven the potential of a single serum β hCG level, at 12 to 14 days after embryo transfer, to predict pregnancy outcomes after In vitro fertilization. However, these studies show significant heterogeneity, with paucity of data from African populations. This study aimed to evaluate the prognostic value of a serum β-hCG level cut off, 12 days after embryo transfer, on predicting livebirth among Ugandan women. Methods: A Retrospective cross-sectional study. 337 fresh IVF cycles with serum β-hCG ≥5 mIU/mL, at 12 days after embryo transfer, were eligible. We abstracted participant characteristics, IVF cycle characteristics, livebirth, clinical pregnancy, and ongoing pregnancy data from each eligible cycle. We utilized the Youden index metric and the maximize_boot_metric method to link serum β-hCG levels to outcome data and determine the optimal cut off values. Results:The optimal serum β-hCG cut off value for predicting livebirth was 437.42mIU/ml with a corresponding sensitivity and false positive rate of 72% and 31% respectively. The cut-offs for clinical and ongoing pregnancy, were 239.58 mIU/ml and 353.66 mIU/ml respectively. These corresponded with a sensitivity of 83% and 77% respectively, and a false positive rate of 27% and 33% respectively. The serum β-hCG cut off had a poor discriminatory performance for predicting live birth but moderate performance for predicting clinical and ongoing pregnancies. Conclusion: A single serum β-hCG 12 days after cleavage embryo transfer has poor discriminatory performance in predicting live birth, albeit performing modestly in predicting clinical pregnancy and ongoing pregnancy among Uganda 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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".