Use of Helical CT Is Associated with an Increased Incidence of Postoperative Pulmonary Emboli in Cancer Patients with No Change in the Number of Fatal Pulmonary Emboli
Bibliographic record
Abstract
BACKGROUND: Multidetector computed tomography (MDCT) scanning technology has increased the ease with which pulmonary emboli (PE) are evaluated. Our aim was to determine whether the incidence and severity of postoperative PE have changed since adoption of multidetector computed tomography. STUDY DESIGN: A prospective postoperative morbidity and mortality database from a single institution was used to identify all cancer patients who experienced a PE within 30 days of thoracic, abdominal, or pelvic operations. The incidence, type (central, segmental, and subsegmental), and severity of PE were examined. RESULTS: A total of 295 PE were documented among 47,601 postoperative cancer patients. The incidence of PE increased yearly from 2.3 per 1,000 patients in 2000 to 9.3 per 1,000 patients in 2005 (p < 0.0001). This corresponded to an increasing number of CT scans of the chest performed (6.6 CT scans per 1,000 postoperative patients in 2000 versus 45 in 2005; p < 0.0001). The increased incidence was because of a 7.8% (CI, 4.0 to 11.7) and 5.4% (CI, 4.1 to 6.7) average annual increase in segmental and subsegmental PE, respectively. There was no change in the number of central (0.1%; CI, -1.0 to 1.12) PE. Overall incidence of fatal PE was 0.4 and did not change during the time period (p = 0.3). A central PE was more commonly associated with hypoxia, ICU admission, and 30-day mortality (33% versus 5% for peripheral; p = 0.02). CONCLUSIONS: Chest CT scans are being performed more frequently on postoperative cancer patients and have resulted in an increased diagnosis of peripheral PE. The clinical significance of, and optimal treatment for, diagnosed subsegmental PE are incompletely defined.
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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 | 0.005 |
| 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 | 0.001 |
| 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".