The Prevalence of Symptomatic and Coincidental Pulmonary Embolism on Computed Tomography
Bibliographic record
Abstract
PURPOSE: To investigate the proportion of pulmonary embolism (PE) on computed tomographic pulmonary angiography (CTPA) and the proportion of coincidental PE on regular contrast-enhanced CT in oncological and nononcological patients. METHODS: This study received internal review board approval and was Health Insurance Portability and Accountability Act compliant. All consecutive adult patients who had contrast-enhanced chest CT or dedicated CTPA during January 2005 were studied. Procedural codes were used to identify cases, and all CT images were reviewed. Clinical data collected included oncology status, chemotherapy regimen, site of tumor, and location of PE. chi2 Tests were used for statistical analysis. RESULTS: Two hundred twenty-nine patients had CTPA, and 27 (11.8%) of them were positive for PE. Of 1168 patients who had contrast-enhanced CT for other indications, coincidental PE was found in 21 patients (1.8%). The proportions of coincidental PE were 3.3% of patients with progressive cancer, 2.5% of patients with stable cancer, 0.7% of patients with no evidence of cancer posttreatment, and 1.0% of nononcological patients. Coincidental PE was found more frequently in patients with progressive cancer compared with nononcological patients (P = 0.035). Patients who were on chemotherapy also had a higher risk of coincidental PE (P=0.019). CONCLUSIONS: The prevalence of symptomatic PE on dedicated CTPA was 11.8%, and the rate of coincidental PE on contrast-enhanced CT was 1.8%. Coincidental PE was significantly higher inpatients with progressive cancer or those receiving chemotherapy.
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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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".