Early Maternal Serum β-human Chorionic Gonadotropin Measurements After ICSI in the Prediction of Long-term Pregnancy Outcomes: A Retrospective Cohort Analysis
Bibliographic record
Abstract
BACKGROUND: Initial low maternal serum β-human chorionic gonadotropin (β-hCG) is a good predictor of early pregnancy demise. Our objective was to determine its predictive value in determining the long-term outcome in ICSI pregnancies. METHODS: A retrospective cohort study was designed at the Saudi Center for Assisted Reproduction. Two hundred and sixty-one women with ICSI pregnancies were followed up from initial β-hCG level determination till the end of pregnancy. Accuracy of early β-hCG in predicting the occurrence of a live-birth, ongoing pregnancy, late miscarriage, ectopic pregnancy and early miscarriage following ICSI was measured. RESULTS: β-hCG levels were significantly different in pregnancies that reached the stage of an ongoing pregnancy and live-birth as compared to early pregnancy loss. The ROC curves demonstrated a high sensitivity for identifying patients with ectopic pregnancies and early miscarriage (100% and 93.33% respectively). The remaining results ranged from a sensitivity of 69% to 79% and specificity of 62% to 75%. CONCLUSIONS: In ICSI pregnancies, a single early β-hCG may help to identify pregnancies that will reach full-term and delivery. KEYWORDS: ICSI; Human chorionic gonadotropin; Outcome; Pregnancy.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| 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 | 0.001 |
| 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".