Predıctıve value of pregnancy of follıcular fluıd <scp>fetuın‐A</scp> and ‐B levels ın infertıle women after intra‐cytoplasmic sperm injection
Bibliographic record
Abstract
AIM: We aimed to investigate the value of follicular fluid fetuins-A and -B to predict successful IVF and pregnancy outcomes in infertile women with poor, normal, and high ovarian reserve. METHODS: The follicular fluid of 96 infertile women who underwent intra-cytoplasmic sperm injection (ICSI) procedure was analyzed. Fetuins-A and -B levels were examined and compared in those who could achieve pregnancy and those who could not. Receiver operating characteristic curve analyzes were used to determine cut-off and statistically significant associations for fetuins-A and -B. RESULTS: Follicular fluid fetuin-A levels were higher in cases with weak ovarian reserve (OR) (p < 0.05) and higher in patients who did not achieve clinical pregnancy (p < 0.05). Conversely, the follicular fluid fetuin-B levels were lower in cases with poor OR (p < 0.05) and were lower in patients who did not achieve a clinical pregnancy (p < 0.05). A follicular fluid fetuin-A concentration ≤ 19.12 ng/mL had a sensitivity and specificity of 94.74% and 93.1%, respectively, at predicting clinical pregnancy. While the follicular fluid fetuin-B concentration >24.7 ng/mL had sensitivity and specificity of 71.1% and 51.7%, respectively, for clinical pregnancy prediction. CONCLUSION: Overall, high levels of follicular fluid fetuin-A may be independently associated with unsuccessful IVF irrespective of OR grouping. A low level of follicular fetuin-B was also associated with failed IVF. The sensitivity and specificity were found to be higher for fetuin-A in predicting clinical pregnancy. Therefore, the follicular fluid fetuin-A may be more predictive for successful IVF and clinical pregnancy outcomes than follicular fluid fetuin-B.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| 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".