Republished: Heart failure disease management programmes: a new paradigm for research
Bibliographic record
Abstract
“The transition from a paradigm in crisis to a new one … changes some of the field's most elementary theoretical generalizations as well as many of its paradigm methods and applications.” Thomas S Kuhn.1 Few areas of non-pharmacological research have had as much study or success as heart failure disease management programmes (HFDMPs). The first pioneering trial published 17 years ago has been followed by over 60 randomised trials and 15 meta-analyses of home, hospital or telehealth interventions.2 Thirteen of these reviews identified 15%–20% improvements in all-cause mortality, nine identified 30%–56% improvements in HF-related hospitalisation, and 10 identified reductions in all-cause readmission of 15%–25%.2 Accordingly, guidelines recommended HFDMPs widely and enthusiastically.3 The effectiveness of these non-pharmacological interventions appears assured. Thomas Kuhn famously argued that periods of relative stability in science, known as ‘Normal Science’, are characterised by such consistent successes.1 However, these periods of scientific successes are frequently disturbed by anomalies that cannot be explained using the very approaches that produced past successes.1
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.010 | 0.033 |
| Meta-epidemiology (narrow) | 0.005 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.003 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.007 | 0.007 |
| Open science | 0.004 | 0.001 |
| Research integrity | 0.012 | 0.026 |
| Insufficient payload (model declined to judge) | 0.014 | 0.011 |
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".