Diagnostic Strategies Incorporating Computed Tomography Angiography for Pulmonary Embolism
Bibliographic record
Abstract
PURPOSE: Pulmonary embolism (PE) is a significant cause of morbidity and mortality. The clinical diagnosis of PE can be quite challenging, necessitating a systematic, evidence-based, and cost-effective approach. MATERIALS AND METHODS: A sensitive search strategy using keywords associated with PE diagnosis and economic evaluation was conducted. The libraries searched included MEDLINE, EMBASE, Health Technology Assessments, NHS Economic Evaluation Database, and the Cochrane Central Register of Clinical Trials. Studies were required to be a model-based cost-effectiveness analysis (CEA) for PE diagnosis. To be included, studies had to have evaluated both the cost and effectiveness of diagnostic algorithms. In addition, computed tomography (CT) had to have been a component in at least 1 possible algorithm. The characteristics of each CEA were extracted. In addition, the characteristics of CT pulmonary angiography were extracted (sensitivity, specificity, and cost). The most cost-effective strategy and its comparator were presented with the corresponding incremental cost-effectiveness ratio. RESULTS: Thirteen studies met our inclusion criteria. Costs were obtained using a variety of methods. Most studies measured effectiveness using a metric of survival, whereas 3 studies used quality-adjusted life years. Studies varied considerably in terms of the quality of economic evaluation. All but 1 study reported that computed tomographic pulmonary angiography (CTPA)-typically combined with ultrasound or D-dimer-was part of the most cost-effective algorithm. CONCLUSIONS: CEA is a useful tool for evaluating potential algorithms for PE diagnosis. Future CEAs would do well to include the use of magnetic resonance angiography and the potential for alternate diagnoses in diagnostic algorithms.
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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 | 0.007 | 0.045 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.006 |
| Bibliometrics | 0.014 | 0.009 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".