Ontario Multidetector Computed Tomographic Coronary Angiography Study
Bibliographic record
Abstract
BACKGROUND: Computed tomographic coronary angiography (CTCA) has gained clinical acceptance for the detection of obstructive coronary artery disease. Although single-center studies have demonstrated excellent accuracy, multicenter studies have yielded variable results. The true diagnostic accuracy of CTCA in the "real world" remains uncertain. We conducted a field evaluation comparing multidetector CTCA with invasive CA (ICA) to understand CTCA's diagnostic accuracy in a real-world setting. METHODS: A multicenter cohort study of patients awaiting ICA was conducted between September 2006 and June 2009. All patients had either a low or an intermediate pretest probability for coronary artery disease and underwent CTCA and ICA within 10 days. The results of CTCA and ICA were interpreted visually by local expert observers who were blinded to all clinical data and imaging results. RESULTS: Using a patient-based analysis (diameter stenosis ≥50%) of 169 patients, the sensitivity, specificity, positive predictive value, and negative predictive value were 81.3% (95% confidence interval [CI], 71.0%-89.1%), 93.3% (95% CI, 85.9%-97.5%), 91.6% (95% CI, 82.5%-96.8%), and 84.7% (95% CI, 76.0%-91.2%), respectively; the area under receiver operating characteristic curve was 0.873. The diagnostic accuracy varied across centers (P < .001), with a sensitivity, specificity, positive predictive value, and negative predictive value ranging from 50.0% to 93.2%, 92.0% to 100%, 84.6% to 100%, and 42.9% to 94.7%, respectively. CONCLUSIONS: Compared with ICA, CTCA appears to have good accuracy; however, there was variability in diagnostic accuracy across centers. Factors affecting institutional variability need to be better understood before CTCA is universally adopted. Additional real-world evaluations are needed to fully understand the impact of CTCA on clinical care. TRIAL REGISTRATION: clinicaltrials.gov Identifier: NCT00371891.
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 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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".