Bibliographic record
Abstract
Heart failure, once lacking in therapies, now has a rich and growing armamentarium that is available to reduce the morbidity and mortality and improve the quality of life for patients. But are we delivering this to patients and what is the evidence (beyond the randomised controlled trials (RCTs) of individual therapies) that we should do so in a systematic and auditable way via quality improvement initiatives? Analyses from England show an association of six hospital-based process measures to that of lower heart failure (HF) readmission rates (but not all-cause hospitalisations).1 Indeed, caution should be exercised as when focus is placed on one disease, and there may be missed opportunities on treating other diseases.2 Notably, individual measures were only modestly linked to better outcomes and the durability of this effect waned over the year as patients spent more time out of hospital than in. But is association causation? To this end, Agarwal and colleagues3 provide insight into this via a systematic review of RCTs that tested the efficacy of hospital-based improvements initiatives.4 They identified 14 RCTs across five countries—all high-income countries (HIC)—that employed a variety of techniques to improve the care of patients with HF. Of interest, two very large RCTs dominated the area and the heterogeneity (both clinical and statistical) limited the ability to numerically combine the results. The authors identified three key findings emanating from this body of work. First, there was substantial heterogeneity in the existing trials and datasets, and therefore this went from a meta-analysis to a narrative review. Second, baseline care of patients (in …
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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 | 0.091 | 0.276 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.003 |
| Bibliometrics | 0.006 | 0.011 |
| Science and technology studies | 0.003 | 0.015 |
| Scholarly communication | 0.016 | 0.018 |
| Open science | 0.004 | 0.010 |
| Research integrity | 0.006 | 0.012 |
| Insufficient payload (model declined to judge) | 0.005 | 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".