The association between cancer diagnosis, care, and outcomes in 1 million patients hospitalized for acute pulmonary embolism
Bibliographic record
Abstract
OBJECTIVES: To evaluate the clinical care provided to cancer patients hospitalized for acute pulmonary embolism (PE), as well as the association between type of cancer, in-hospital care, and clinical outcomes. METHODS: This study examined the in-hospital care (systemic thrombolysis, catheter-directed thrombolysis, and surgical thrombectomy/embolectomy) and clinical outcomes (mortality, major bleeding, and hemorrhagic stroke) among adults hospitalized due to acute PE between October 2015 to December 2018 using the National Inpatient Sample (NIS). Multivariable logistic regression analysis was used to determine adjusted odds ratios (aOR) with 95% confidence interval (95% CI). RESULTS: Of 1,090,130 hospital records included in the analysis, 216,825 (19.9%) had current cancer diagnoses, including lung (4.7%), hematological (2.5%), colorectal (1.6%), breast (1.3%), prostate (0.8%), and 'other' cancer (9.0%). Cancer patients had lower adjusted odds of receiving systemic thrombolysis, catheter-directed therapy, and surgical thrombectomy/embolectomy compared with their non-cancer counterparts (P < 0.001), except for systemic thrombolysis (aOR 0.96, 95% CI 0.85-1.09, P = 0.553) and catheter-directed therapy (aOR 0.82, 95% CI 0.67-1.00, P = 0.053) for prostate cancer. Cancer patients had greater odds of mortality (P < 0.05). Lung cancer patients had the highest odds of mortality (aOR 2.68, 95% CI 2.61-2.76, P < 0.001) and hemorrhagic stroke (aOR 1.75, 95% CI 1.61-1.90, P < 0.001), while colorectal cancer patients had the greatest odds of bleeding (aOR 2.04, 95% CI 1.94-2.15, P < 0.001). CONCLUSION: Among those hospitalized for PE, cancer diagnoses were associated with lower odds of invasive management and poorer in-hospital outcomes, with metastatic status being an especially important determinant. Appropriateness of care could not be assessed in this study.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".