Diagnostic Strategies Incorporating Computed Tomography Angiography for Pulmonary Embolism
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.004 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it