January 2021 at a glance: focus on sex differences, acute heart failure and exercise capacity
Bibliographic record
Abstract
Sex differencesSex differences have a major impact on management and prognosis of heart failure (HF) patients.1,2 An analysis of UK national health registries, including >50 000 HF patients from the years 2000 to 2017, showed that women were older than men at the time of diagnosis (79.6 vs. 74.8years), but had a better prognosis after age adjustement.3 A systematic review of randomized controlled trials, including 183 097 HF patients with reduced ejection fraction (HFrEF), showed that women were under-enrolled in most of the studies representing only 25.5% of the patients.The trend is not changing over time.4 Acute heart failureDespite advances in treatment, 5 outcome remains poor in acute HF patients.6 A study conducted in Australia and New Zealand showed that 1 out of 10 patients died within 30 days of their last HF hospitalization
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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 | 0.017 |
| 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 | 0.006 | 0.005 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.182 | 0.060 |
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".