Comparison of short and long forms of the Flinders program of chronic disease SELF-management for participants starting SGLT-2 inhibitors for congestive heart failure (SELFMAN-HF): protocol for a prospective, observational study
Bibliographic record
Abstract
Introduction Congestive heart failure (CHF) causes significant morbidity and mortality. It is an epidemic, and costs are escalating. CHF is a chronic disease whose trajectory includes stable phases, periods of decompensation, and finally palliation. Health services and medical therapies must match the various patient needs. Chronic disease self-management (CDSM) programmes that are patient-focused, identify problems and set actionable goals that appear as a logical, cost-friendly method to navigate patient journeys. There have been challenges in standardising and implementing CHF programmes. Methods and analysis SELFMAN-HF is a prospective, observational study to evaluate the feasibility and validity of the SCRinHF tool, a one-page self-management and readmission risk prediction tool for CHF, with an established, comprehensive CDSM tool. Eligible patients will have CHF with left ventricular ejection fraction <40% and commenced sodium glucose co-transporter-2 inhibitors (SGLT2-i) within 6 months of recruitment. The primary endpoint is the 80% concordance in readmission risk predicted by the SCRinHF tool. The study will recruit >40 patients and is expected to last 18 months. Ethics and dissemination This study has been approved by the St Vincent’s ethics committee (approval no. LRR 177/21). All participants will complete a written informed consent prior to enrolment in the study. The study results will be disseminated widely via local and international health conferences and peer-reviewed publications.
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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 | 0.046 | 0.035 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.006 | 0.005 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.037 | 0.007 |
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".