The POPI-Plus tool: prediction model of outcome of pregnancy in in vitro fertilization from a large retrospective cohort
Bibliographic record
Abstract
OBJECTIVE: To create a tool that accurately predicts live birth chances after a positive pregnancy test after elective single embryo transfer (ET). DESIGN: Retrospective cohort. SETTING: CHUM hospital and Ovo clinic in Montreal, Canada. PATIENT(S): Patients with a positive pregnancy test result who underwent their first single ET after in vitro fertilization (IVF) at the CHUM hospital and Ovo clinic in Montreal, Canada, from 2012 to 2016 were selected. A total of 1,995 patients were included in this study. INTERVENTION(S): The data from both centers were combined and divided into training (70%, n = 1,398) and validation (30%, n = 597) sets. The predictive model was developed using backward selection method for the following variables: age of patient at egg retrieval; log β-human chorionic gonadotropin (β-hCG) (β-hCG) 1; log β-hCG 2; and IVF treatment type. Moreover, the classification tree, random forest, and neural network models were generated. MAIN OUTCOME MEASURE(S): The measured outcomes were live birth (live fetus ≥24 weeks of gestation) and nonviable pregnancies. The performance of all models was evaluated by area under the receiver operating characteristic curve (AUC). RESULT(S): Advancing age was negatively correlated with live birth. The odds ratio (OR) of age of patient at the time of egg retrieval was 0.95 (95% confidence interval [CI], 0.91-0.99). The log β-hCG 1 and log β-hCG 2 were positively correlated with live birth in the univariate analysis (OR, 4.15 [95% CI, 3.19-5.39], and OR, 3.84 [95% CI, 2.99-4.93], respectively). The β-hCG 1 level needed for a successful pregnancy was lower in frozen ET and modified natural IVF than in simulated IVF (OR, 0.55 [95% CI, 0.34-0.91], and OR, 0.49 [95% CI, 0.26-0.95], respectively). The best performance in terms of the AUC was the updated logistic model: POPI-Plus. The AUC values were 0.76 (95% CI, 0.73-0.79) and 0.78 (95% CI, 0.74-0.82) for the training and validation data, respectively. The other models (classification tree, random forest, and neural network) also performed adequately, with an AUC of ≥0.7, but remained below POPI-Plus. An open-access calculator was generated and can be found on the website of the University of Montreal on the following link: https://deptobsgyn.umontreal.ca/departement/divisions/medecine-et-biologie-de-la-reproduction/the-popi-plus-tool/. CONCLUSION(S): The POPI-Plus tool offers individualized counseling for patients after an initial positive β-hCG test result. Future studies will assess its impact on patient anxiety while awaiting viability ultrasound and perform prospective validation on new patients.
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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 | 0.005 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".