Short- and long-term mortality after pulmonary embolism in patients with and without cancer
Bibliographic record
Abstract
Pulmonary embolism (PE) is a major cause of mortality and morbidity. It is known that the risk of death varies by provoking factors; however, it is unknown if the risk of death persists beyond the initial diagnosis among patients with cancer-associated and non-cancer provoked patients. In this study, we aimed to investigate the effect of cancer on overall, short- and long-term mortality in a cohort of consecutive incident PE patients. Using administrative databases, we identified all incident cases of PE between 2004 and 2012 in Alberta, Canada. Cases were stratified by provoking factors (i.e. unprovoked, provoked, and cancer-associated). A multivariate Cox survival model was used to estimate the hazard ratios of short- and long-term death. We identified 8641 patients with PE, among which 42.2% were unprovoked, 37.9% were provoked and 19.9% were cancer-associated. The 1-year and 5-year survival probabilities were 60% (95% CI: 57-64%) and 39% (95% CI: 36-43%) in patients with cancer-associated PE, 93% (95% CI: 92-94%) and 80% (95% CI: 78-81%) in provoked PE, and 94% (95% CI: 93-95%) and 85% (95% CI: 83-87%) in unprovoked PE, respectively. Compared to patients with unprovoked events, both short-term and long-term survival in patients with cancer-associated PE have a higher observed risk of all-cause mortality in all age groups ( p<0.001). In contrast, patients with provoked events had a similar short- and long-term all-cause mortality. While PE has a significant mortality in all risk groups, patients with cancer have a higher risk of short-term mortality compared to patients with unprovoked PE.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 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".