Rates of downstream invasive coronary angiography and revascularization: computed tomographic coronary angiography vs. Tc-99m single photon emission computed tomography
Bibliographic record
Abstract
AIMS: Computed tomographic coronary angiography (CTA) appears to be a useful modality for the detection of obstructive coronary artery disease (CAD). Recent data suggest that CTA may reduce the frequency of normal invasive coronary angiograms. However, there remains concern that the implementation of CTA could increase referrals to invasive coronary angiography (ICA). To further support the clinical acceptance of CTA, it is important to compare CTA to another accepted modality such as single photon emission computed tomography (SPECT). We followed a cohort of 64-slice CTA patients and a matched cohort of Tc-99m SPECT patients to determine downstream referrals for ICA and revascularization. METHODS AND RESULTS: Consecutive CTA patients (without history of revascularization or cardiac transplantation) were prospectively enrolled and compared with a Tc-99m SPECT cohort (matched for age, gender, and Morise score). Each CTA and SPECT was evaluated for obstructive CAD and patients were followed for downstream ICA and revascularization. Of the 1221 patients in each cohort, 129 (10.6%) CTA patients and 125 (10.2%) SPECT patients were referred to ICA. Of those referred to ICA, obstructive CAD was confirmed in 105 (81.4%) CTA patients and in 88 (70.4%) SPECT patients. Differences in false positive rates were significantly lower in the CTA than the SPECT cohort (9.7 and 25.8%, respectively, P = 0.009). Rates of revascularization were similar in the CTA and SPECT cohorts (6.2 vs. 5.9%, respectively). CONCLUSION: Compared with SPECT, CTA had similar referrals for ICA and revascularization rates but lower false positive rates. Computed tomographic coronary angiography appears to be a viable non-invasive diagnostic modality and does not appear to negatively impact upon ICA resources.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".