OUTSMART HF
Bibliographic record
Abstract
Background: Cardiac magnetic resonance (CMR) is a recommended imaging test for patients with heart failure (HF); however, there is a lack of evidence showing incremental benefit over transthoracic echocardiography. Our primary hypothesis was that routine use of CMR will yield more specific diagnoses in nonischemic HF. Our secondary hypothesis was that routine use of CMR will improve patient outcomes. Methods: Patients with nonischemic HF were randomized to routine versus selective CMR. Patients in the routine strategy underwent echocardiography and CMR, whereas those assigned to selective use underwent echocardiography with or without CMR according to the clinical presentation. HF causes was classified from the imaging data as well as by the treating physician at 3 months (primary outcome). Clinical events were collected for 12 months. Results: A total of 500 patients (344 male) with mean age 59±13 years were randomized. The routine and selective CMR strategies had similar rates of specific HF causes at 3 months clinical follow-up (44% versus 50%, respectively; P =0.22). At image interpretation, rates of specific HF causes were also not different between routine and selective CMR (34% versus 30%, respectively; P =0.34). However, 24% of patients in the selective group underwent a nonprotocol CMR. Patients with specific HF causes had more clinical events than those with nonspecific caused on the basis of imaging classification (19% versus 12%, respectively; P =0.02), but not on clinical assessment (15% versus 14%, respectively; P =0.49). Conclusions: In patients with nonischemic HF, routine CMR does not yield more specific HF causes on clinical assessment. Patients with specific HF causes from imaging had worse outcomes, whereas HF causes defined clinically did not. Registration: URL: https://www.clinicaltrials.gov ; Unique identifier: NCT01281384.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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) | 0.037 | 0.003 |
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".