Computed tomography coronary angiography for patients with heart failure (CTA-HF): a randomized controlled trial (IMAGE-HF 1C)
Bibliographic record
Abstract
AIMS: This randomized controlled trial sought to determine the financial impact of an initial diagnostic strategy of coronary computed tomography angiography (CCTA) in patients with heart failure (HF) of unknown aetiology. Invasive coronary angiography (ICA) is used to investigate HF patients. CCTA may be a non-invasive cost-effective alternative to ICA. This randomized controlled trial sought to determine the financial impact of an initial diagnostic strategy of coronary computed tomography angiography (CCTA) in patients with heart failure (HF) of unknown aetiology. METHODS AND RESULTS: This multicentre, international trial enrolled patients with HF of unknown aetiology. The primary outcome was the cost of CCTA vs. ICA strategies at 12 months. Clinical outcomes were also collected. An 'intention-to-diagnose' analysis was performed and a secondary 'as-tested' analysis was based on the modality received. Two hundred and forty-six patients were randomized (age = 57.8 ± 11.0 years, ejection fraction = 30.1 ± 10.1%). The severity of coronary artery disease was similar in both groups. In the 121 CCTA patients, 93 avoided ICA. Rates of downstream ischaemia and viability testing were similar for both arms. There were no significant differences in the composite clinical outcomes or quality of life measures. The cost of CCTA trended lower than ICA [CDN -$871 (confidence interval, CI -$4116 to $3028)]. Using an 'as-tested' analysis, CCTA was associated with a decrease in healthcare costs (CDN -$2932, 95% CI -$6248 to $746). CONCLUSION: In patients with HF of unknown aetiology, costs were not statistically different between the CCTA and ICA strategies. CLINICAL TRIALS.GOV: NCT01283659.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.009 | 0.001 |
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".