July 2020 at a Glance: Focus on Imaging and Cardiomyopathies
Bibliographic record
Abstract
Data on epidemiology of cardiomyopathies are limited.1–3 Merlo et al.4 showed a reduction in mortality, heart transplantation, left ventricular (LV) assist device implantation and sudden cardiac death in the last decade, compared with the previous ones, in patients with non-ischaemic dilated cardiomyopathy (DCM). Outcomes were also dependent on specific phenotypes with the poorest in patients with chemotherapy-induced DCM and the best in those with tachycardia-induced DCM. An analysis of the Swedish Heart Failure (HF) Register of the young patients with HF, compared with the others, showed that patients aged <55 years were more likely to have DCM, obesity, congenital heart disease, and a low LV ejection fraction (LVEF). Patients aged <55 years had a five times higher mortality risk compared with control subjects with the highest risk in the youngest ones, 18-34 years old.5 Diabetes is a known risk factor for cardiovascular death in patients with a recent myocardial infarction.6 Shin et al.7 showed the independent prognostic value for HF hospitalizations or cardiovascular death of glycated haemoglobin (A1C) levels (adjusted hazard ratio 1.11, 95% confidence interval 1.01–1.21 per 1% higher A1C) and LVEF in patients with type 2 diabetes and a recent acute coronary syndrome.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.287 | 0.140 |
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".