Patients Presenting to the Emergency Department with Bleeding in Early Pregnancy: Comparing Different Models to Predict Pregnancy Success
Bibliographic record
Abstract
OBJECTIVES: Bleeding in early pregnancy is a common obstetric presentation in the emergency department (ED), and the outcome is difficult to predict. We developed and compared random forest machine learning (Live Birth Risk Score [LiBRisk]) and nomogram models for predicting the likelihood of a live birth among women presenting at 3 Canadian EDs with bleeding in early pregnancy. METHODS: Data were prospectively collected on 200 patients with bleeding in early pregnancy using a structured questionnaire, medical record review, and administrative databases. We developed the nomogram with variables selected via multivariable logistic regression analysis. LiBRisk was built using the Shapley variable importance cloud (ShapleyVIC) to derive a simple point-based clinical risk scoring system. RESULTS: Overall, 115 (55%) patients experienced a miscarriage. We excluded duration of vaginal bleeding and pain score, which did not enhance model performance, and constructed LiBRisk with the 8 most important variables (β-human chorionic gonadotrophin level, age, gestational age, gravidity, parity, proportionality of uterine size to gestational age, abdominal cramping, and number of prior spontaneous abortions). All 10 variables were included in the nomogram. The area under the receiver operating characteristic curve of LiBRisk in the test and validation sets were 0.913 (95% CI 0.907-0.919) and 0.900 (95% CI 0.887-0.913), respectively. The C-index of the nomogram was 0.720 (95% CI 0.714-0.726) and 0.860 (95% CI 0.853-0.867) in the test and validation sets, respectively. LiBRisk outperformed the nomogram in all metrics. CONCLUSIONS: We developed and compared LiBRisk and nomogram models for determining the probability of eventual pregnancy success/failure in women presenting to the ED with bleeding in early pregnancy. LiBRisk was more parsimonious, incorporating only 8 variables, and outperformed the nomogram in all metrics. Given these promising results, further testing seems warranted.
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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 | 0.016 | 0.030 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".