Cost-Effectiveness of Currently Accepted Strategies for Pulmonary Embolism Diagnosis
Bibliographic record
Abstract
Improvements in the methods of clinical trials combined with the use of objective tests to detect venous thrombosis have enhanced the clinician's ability to diagnose pulmonary embolism and venous thrombosis (venous thromboembolism). The authors updated a previous cost-effectiveness analysis of the commonly recommended strategies for pulmonary embolism diagnosis and management to reflect current clinical practice. Two criteria of effectiveness were used: correct identification of venous thromboembolism and correct identification of venous thromboembolism and correct identification of patients for whom treatment was unnecessary. The cost of each diagnostic alternative was defined as the direct cost of administering the diagnostic tests plus the treatment costs associated with a positive test result. A strategy based on the combined use ofventilation-perfusion lung scanning, serial ultrasonography, cardiorespiratory evaluation, and pulmonary angiography was the most cost-effective. This strategy also necessitated pulmonary angiography in the fewest number of patients. The safety of this strategy relates to two important biologic concepts: 1) local extension of submassive pulmonary embolism in the lung is not an important cause of morbidity or mortality in patients with adequate cardiorespiratory reserve, and 2) in most patients, proximal vein thrombi of the lower extremities are the source of recurrent pulmonary embolism.
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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 | 0.004 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.000 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".