Improving Heart Failure Health: Is there a Secret Swedish Sauce?
Bibliographic record
Abstract
This article refers to 'Association between enrolment in a heart failure quality registry and subsequent mortality-a nationwide cohort study' by L.H. Lund et al., published in this issue on pages 1107-1116.Heart failure (HF) is a paradigm case for the challenges of 21st century health care as a result of its growing prevalence, the increasing complexity of care required, high costs associated and opportunities for improved outcomes.In both Europe and the USA, the delivery of evidence-based HF care varies significantly across clinicians and health systems, 1,2 yet this has been well documented over the last two decades.What is often lacking subsequent to the identification of evidence on what optimal care should consist of is action that represents the next step towards establishing evidence for how the best care can be implemented in a timely, efficient, equitable and effective manner.In this issue of the journal, Lund et al. 3 provide compelling data indicating that patients enrolled in SwedeHF received better evidence-based HF care with potentially better outcomes than those not enrolled.In this study, SwedeHF enrolled 9.5% of 231 437 patients (68% of whom were inpatients) with incident HF diagnosed during the period 2006 to 2013 across 64 of 75 potential hospitals in Sweden.3 SwedeHF is a voluntary registry that publicly reports on HF inpatient and outpatient care to enable quality improvement (QI) through the gathering of a wealth of data.In the current study, the authors found marked differences in the use of medical therapy for chronic HF with reduced ejection fraction (HFrEF): ACE inhibitors or angiotensin II receptor blockers (ARBs) were used in 82% of SwedeHF patients compared with 56% of non-SwedeHF patients, beta-blockers in 84% vs. 60%, and aldosterone antagonists in 33% vs. 18%.SwedeHF patients had a significantly lower risk for all-cause mortality compared with non-SwedeHF patients (unadjusted hazard ratio 0.65, 95% confidence interval 0.63-0.66),although the hazard attenuated with adjustment for baseline differences and there was no difference after adjusting for the above differences in medical therapy for chronic HFrEF.In addition, the authors found that other evidence-based use of cardiovascular medication was higher in SwedeHF patients and is likely to play The opinions expressed in this article are not necessarily those of the Editors of the European Journal of Heart Failure or of the European Society of Cardiology.
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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 | 0.021 | 0.066 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.003 | 0.005 |
| Scholarly communication | 0.013 | 0.009 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.007 | 0.010 |
| Insufficient payload (model declined to judge) | 0.016 | 0.005 |
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".