Assessment of inpatient multimodal cardiac imaging appropriateness at large academic medical centers
Bibliographic record
Abstract
BACKGROUND: Responding to concerns regarding the growth of cardiac testing, the American College of Cardiology Foundation (ACCF) published Appropriate Use Criteria (AUC) for various cardiac imaging modalities. Single modality cardiac imaging appropriateness has been reported but there have been no studies assessing the appropriateness of multiple imaging modalities in an inpatient environment. METHODS: A retrospective study of the appropriateness of cardiac tests ordered by the inpatient General Internal Medicine (GIM) and Cardiology services at three Canadian academic hospitals was conducted over two one-month periods. Cardiac tests characterized were transthoracic echocardiography (TTE), transesophageal echocardiography (TEE), single-photon emission tomography myocardial perfusion imaging (SPECT), and diagnostic cardiac catheterization. RESULTS: Overall, 553 tests were assessed, of which 99.8% were classifiable by AUC. 91% of all studies were categorized as appropriate, 4% may be appropriate and 5% were rarely appropriate. There were high rates of appropriate use of all modalities by GIM and Cardiology throughout. Significantly more appropriate diagnostic catheterizations were ordered by Cardiology than GIM (93% vs. 82%, p = <0.01). Cardiology ordered more appropriate studies overall (94% vs. 88%, p = 0.03) but there was no difference in the rate of rarely appropriate studies (3% vs. 6%, p = 0.23). CONCLUSION: The ACCF AUC captured the vast majority of clinical scenarios for multiple cardiac imaging modalities in this multi-centered study on Cardiology and GIM inpatients in the acute care setting. The rate of appropriate ordering was high across all imaging modalities. We recommend further work towards improving appropriate utilization of cardiac imaging resources focus on the out-patient setting.
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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 | 0.003 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".