P–695 Establishing predictors of the mode of conception in fertility patients presenting with a clinical pregnancy
Bibliographic record
Abstract
Abstract Study question What are the predictors for pregnancies conceived spontaneously (SC), by ovulation induction+/-insemination (OI±IUI) or via In-Vitro Fertilization(IVF), and what proportion of pregnancies were conceived with each method? Summary answer Pregnancies were conceived by SC(27.7%), OI±IUI(33%) or IVF(39.2%).Unexplained infertility positively-predicted SC and OI±IUI-conceptions. Male factor-infertility demonstrated the opposite trend, positively predicting IVF. Endometriosis negatively-predicted SC. What is known already Spontaneous conception (SC) occurs regularly among infertility patients. Most studies have evaluated predictors of pregnancy among women with infertility who were trying to conceive. Few studies have addressed the role of different factors on the mode of conception in infertility patients who were pregnant. Factors found in some studies to be related with a SC were younger female age, shorter duration of infertility, fewer failed IVF cycles, and diagnosis of unexplained-infertility. Study design, size, duration We conducted a retrospective cohort study at a University fertility-center over a six-month period in 2019 and 2020. We reviewed viability scans of 285-patients. Mode of conception was recorded as Spontaneous, OI±IUI, or IVF. Patients’ demographics, obstetric and fertility diagnosis as well as base-line hormones and ovarian reserve testing were extracted to calculate predictors for the mode of conception. Pregnancy was defined as an intra-uterine fetal sac on a transvaginal ultrasound in the 1st-trimester. Participants/materials, setting, methods Parametric analysis was done using ANOVA and Tukey’s post-hoc test. Nonparametric analysis was performed using the chi-square test. Predictors of the mode of conception were calculated by multivariate regression analysis using the variables not in the equation model including the following parameters: male and female age, gravidity, parity, ectopic-pregnancies, infertility diagnosis, baseline serum: FSH, estradiol, TSH, AMH, and AFC. Data is presented as mean ±SD or percentage. P < 0.05 was significant. IRB approval was obtained. Main results and the role of chance 79 (27.7%) of pregnancies were SC, 94 (33%) resulted from OI±IUI, and 112 (39.2%) from IVF. Demographics didn’t differ between the groups including: female age(p = 0.06), male age(p = 0.79), gravidity (p = 0.47), parity(p = 0.7), ectopic-pregnancies(p = 0.07), baseline serum FSH(p = 0.29), estradiol(p = 0.65), TSH(p = 0.56), AMH(p = 0.42), and AFC(p = 0.06). Infertility diagnoses differed when comparing SC, OI±IUI and IVF conceptions respectively: Unexplained (22.7%, 22.3%, 15.1%, p = 0.03), Male-Factor(MF) (25%, 27.6%, 42.8%, p = 0.042), Tubal-factor (2.5%, 2.1%, 13.4, p = 0.002) and Ovulation-disorders/PCOS (24%, 32%, 12.5% p = 0.002). Endometriosis trended higher in women with IVF (p = 0.09). A positive predictor for SC was unexplained infertility(p = 0.0001). A negative predictor was endometriosis(p = 0.005). SC was sub-significantly less likely in the presence of MF (p = 0.057). Unexplained-infertility was a positive predictor for OI±IUI pregnancies(p = 0.047), whereas MF was a negative predictor(p = 0.0001). As for IVF-conceptions, MF was a positive predictor(p = 0.008), while unexplained-infertility negatively predicted conception by IVF(p = 0.018). Ovulation-disorders/PCOS trended lower in women with IVF (p = 0.052). While baseline serum estradiol levels were similar between groups (means 194–218pmol/L), multivariate regression showed it to be a predictor for OI±IUI and IVF conceptions. The clinical significance of this finding is not clear. Interestingly, female age and ovarian reserve were not found to predict one type of conception over another. Other possible predictors in the model were not significant. Limitations, reasons for caution This retrospective cohort may hide underlying bias. Clinical pregnancies were evaluated and not live birth. Our cohort represents patients that conceived and do not offer information about the entire sub-fertile population that is treated in our center, which is also a strength as it’s a novel way of evaluating predictors. Wider implications of the findings: Among patients that conceived spontaneously, advanced age and ovarian reserve did not play a negative role. Predictors of pregnancy were confirmed as expected with the majority of unexplained infertility conceptions occurring spontaneously or with OI+/-IUI, patients with Male factor infertility often conceived by IVF, and ovulation disorders by OI+/-IUI. Trial registration number NA
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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) | 0.000 | 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 teacher head, 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".