Incidence and Clinical Outcomes of Occult Malignancies Detected by Computed Tomographic Pulmonary Angiography in Patients with Acute Pulmonary Embolism.
Bibliographic record
Abstract
Abstract Abstract 2983 Poster Board II-959 Introduction: Venous thromboembolism (VTE) can be the earliest sign of malignancy. Approximately 10% of patients with unprovoked VTE will be diagnosed with cancer within the next 12 months. Diagnosis of pulmonary embolism (PE) using computed tomographic pulmonary angiograhy (CTPA) allows the visualization of anatomy in addition to thoracic vasculature. Hence, CTPA might be useful for detecting occult cancers in patients with PE. Objective: To evaluate the incidence and clinical outcomes of occult cancers detected by CTPA in patients with acute PE. Methods: This is a retrospective cohort study of consecutive patients with suspected PE undergoing CTPA at the Ottawa Hospital from Jan 1, 2007 to Dec 31, 2008. PE was defined as a subsegmental or larger pulmonary artery filling defect on CTPA. Occult malignancy was defined as any new cancer first detected by CTPA with index PE diagnosis in patients with no known history of malignancy. All patients were followed for a minimum of 6 months after the index PE. Results: A total of 4410 CTPA were reviewed and 748 (17%) were positive for acute PE. Of these, 57 (7.6%; 95% CI: 5.7 to 9.5) revealed abnormalities suggestive of possible occult cancers. Twenty-two (2.9%; 95% CI: 1.7 to 4.2) patients were diagnosed with occult cancers. Among these 22 patients with occult cancers, 20 (91%) had unprovoked PE. Thirteen (59%) patients had occult lung cancer and 16 (73%) had advanced stage (stage 3 or 4) cancers. Sixteen (73%) patients have died following the diagnosis of occult cancer, and their median survival following the diagnosis of PE was 51 days. Conclusion: CTPA detected occult cancers in approximately 3% of patients with acute PE. Most of these cancers were detected at an advanced stage and were associated with high mortality and short survival. Disclosures: No relevant conflicts of interest to declare.
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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 | 0.000 | 0.004 |
| 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 | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".