Impact of reliance on CT pulmonary angiography on diagnosis of pulmonary embolism: A Bayesian analysis
Bibliographic record
Abstract
BACKGROUND: Spiral computed tomographic pulmonary angiography (CTPA) has become the primary test used to investigate suspected pulmonary embolism (PE) at many institutions, despite uncertainty regarding its sensitivity and specificity. Although CTPA-based diagnostic algorithms focus on minimizing the false-negative rate, we hypothesized that increasing use of CTPA also might lead to false-positive diagnoses. OBJECTIVE: Determine the frequency of possible false-positive diagnoses of PE when CTPA is the primary diagnostic test. DESIGN: Retrospective cohort study. SETTING: Two academic teaching hospitals. PARTICIPANTS: 322 patients with suspected PE evaluated with CTPA. MEASUREMENTS: We used a validated prediction rule to determine the pretest probability of PE in each patient. We combined these pretest probabilities with published estimates of CTPA test characteristics to generate expected posttest probabilities of PE. We compared these posttest probabilities to actual treatment decisions to determine the rate of false-positive diagnoses of PE. RESULTS: Among 322 patients investigated for PE, 37 (12%) had high pretest probability, 101 (32%) moderate, and 184 (57%) low. CT scans were interpreted as positive for PE in 57 patients (17.8%). Regardless of the pretest probability of PE, 96.5% of patients with a positive CTPA were treated with anticoagulants. Even under an optimistic assumption of CTPA test characteristics, as many as 25.4% of these patients may have been treated unnecessarily as a result of a false-positive diagnosis. Most of these patients had a low pretest probability of PE. CONCLUSIONS: Failure to utilize Bayesian reasoning when interpreting CTPA may lead to false-positive diagnoses of pulmonary embolism in a substantial proportion of patients.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.067 | 0.285 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".