Pre-screening for non-diagnostic coronary computed tomography angiography
Bibliographic record
Abstract
Aims: Indiscriminate coronary computed tomography angiography (CCTA) referrals for suspected coronary artery disease could result in a higher rate of equivocal and non-diagnostic studies, leading to inappropriate downstream resource utilization or delayed time to diagnosis. We sought to develop a simple clinical tool for predicting the likelihood of a non-diagnostic CCTA to help identify patients who might be better served with a different test. Methods and results: We developed a clinical scoring system from a cohort of 21 492 consecutive patients who underwent CCTA between February 2006 and May 2021. Coronary computed tomography angiography study results were categorized as normal, abnormal, or non-diagnostic. Multivariable logistic regression analysis was conducted to produce a model that predicted the likelihood of a non-diagnostic test. Machine learning (ML) models were utilized to validate the predictor selection and prediction performance. Both logistic regression and ML models achieved fair discriminate ability with an area under the curve of 0.630 [95% confidence interval (CI) 0.618-0.641] and 0.634 (95% CI 0.612-0.656), respectively. The presence of a cardiac implant and weight >100 kg were among the most influential predictors of a non-diagnostic study. Conclusion: We developed a model that could be implemented at the 'point-of-scheduling' to identify patients who would be best served by another non-invasive diagnostic test.
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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 | 0.009 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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 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".