Refining risk in normotensive acute pulmonary embolism
Bibliographic record
Abstract
Introduction: Normotensive acute pulmonary embolism (aPE) has a wide spectrum of outcomes. The best method to identify patients at higher-risk remains unclear. Aims and Objectives: 1) develop a unique prognostic model for adverse outcomes in normotensive aPE, 2) validate the Bova score in a North American population. Methods: This was a multi-centre retrospective cohort of all aPE admitted from emergency departments in Calgary, Canada between 2012-2017. Logistic regression models with bootstrapping for internal validation were used to predict the composite primary outcome of in-hospital death or hemodynamic decompensation. Results: 2067 patients with normotensive aPE were assessed. A primary outcome occurred in 32 patients (1.5%). Stratified by simplified pulmonary embolism severity index (sPESI), 21.2% were low-risk (0% event rate) and 78.8% were high-risk (2.0% event rate). The multivariable model in sPESI high-risk patients (n=1179) retained high-sensitivity troponin ≥50 pg/ml, CT right-left ventricular diameter ratio ≥1.5, systolic blood pressure <100 mmHg, central pulmonary artery clot, & heart rate ≥100 beats per minute (c-index 0.88, 95% CI 0.82-0.93). Three risk groups were derived from the model using a weighted score (score, prevalence, event rate): group 1 (0-3, 73.8%, 0.34%), group 2 (4-6, 17.6%, 5.8%), group 3 (7-9, 8.65%, 12.8%) (c-index 0.85, CI 0.78-0.91). The prevalence (event rate) by Bova risk categories (n=1482) were: stage 1 55.7% (0.1%), stage 2 29.6% (2.3%) and stage 3 14.6% (7.8%) (c-index 0.80, CI 0.74-0.86). Conclusions: Our novel risk score discriminated normotensive aPE patients at high risk of in-hospital adverse events better than the Bova score. Further validation of our score is 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 | 0.004 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".