Patterns of Left-Ventricular Function Assessment in Patients With Acute Coronary Syndromes
Bibliographic record
Abstract
BACKGROUND: In patients with acute coronary syndromes (ACS), guidelines recommend the assessment of left-ventricular ejection fraction (LVEF). Many patients with ACS undergo multiple assessments of LVEF, the clinical value of which is unknown. METHODS: Patients with ACS undergoing cardiac catheterization between 2012 and 2016 were evaluated and assessments of LV function identified. To evaluate changes in LVEF over time, available echocardiograms were reviewed in a subsample of patients with LVEF data available (n = 3221). Patients with ACS were classified into 3 groups: group 1 (LVEF > 50%), group 2 (LVEF 35% to 50%), and group 3 (LVEF < 35%). RESULTS: Our cohort consisted of 8327 patients with ACS (76% men), presenting with a mean age of 62.4 ± 12.4 years. At index presentation, 66% of patients had an LVEF > 50%, 27% had an LVEF between 35% and 50%, and 7% had severely reduced LVEF of < 35%. More than half of the cohort (n = 4600) had follow-up assessment of LV function, performed over an average of 2.71 ± 1.31 years. In the subsample of 3221 patients, only 1.1% of those in group 1, and 5.1% of those in group 2, deteriorated to an LVEF < 35%. CONCLUSIONS: Patients with ACS often undergo multiple assessments of LV function. Those with initially preserved EF rarely demonstrate a decline in EF to < 35%. A reduction in low-value cardiac tests may be an important first step in improving the quality of care for patients with ACS.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| 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 | 0.000 | 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".