Rapid prediction of adverse outcomes for acute normotensive pulmonary embolism: derivation of the Calgary Acute Pulmonary Embolism score
Bibliographic record
Abstract
Background Acute pulmonary embolism (PE) has a wide spectrum of outcomes, but the best method to risk-stratify normotensive patients for adverse outcomes remains unclear. Methods A multicentre retrospective cohort study of acute PE patients admitted from emergency departments in Calgary, Canada, between 2012 and 2017 was used to develop a refined acute PE risk score. The composite primary outcome of in-hospital PE-related death or haemodynamic decompensation. The model was internally validated using bootstrapping and the prognostic value of the derived risk score was compared to the Bova score. Results Of 2067 patients with normotensive acute PE, the primary outcome (haemodynamic decompensation or PE-related death) occurred in 32 (1.5%) patients. In simplified Pulmonary Embolism Severity Index high-risk patients (n=1498, 78%), a multivariable model used to predict the primary outcome retained computed tomography (CT) right–left ventricular diameter ratio ≥1.5, systolic blood pressure 90–100 mmHg, central pulmonary artery clot and heart rate ≥100 beats·min−1with a C-statistic of 0.89 (95% CI 0.82–0.93). Three risk groups were derived using a weighted score (score, prevalence, primary outcome event rate): group 1 (0–3, 73.8%, 0.34%), group 2 (4–6, 17.6%, 5.8%), group 3 (7–9, 8.7%, 12.8%) with a C-statistic 0.85 (95% CI 0.78–0.91). In comparison the prevalence (primary outcome) by Bova risk stages (n=1179) were stage I 49.8% (0.2%); stage II 31.9% (2.7%); and stage III 18.4% (7.8%) with a C-statistic 0.80 (95% CI 0.74–0.86). Conclusions A simple four-variable risk score using clinical data immediately available after CT diagnosis of acute PE predicts in-hospital adverse outcomes. External validation of the Calgary Acute Pulmonary Embolism score is required.
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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 | 0.002 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| 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".