1255Matching delivery of heart failure management to overcome individual barriers to optimal health care: A case of so CLOSE and yet so far
Bibliographic record
Abstract
Background: Despite a tendency to apply different models of heart failure (HF) management according to a patient's proximity to health services, a number of other factors (beyond geography) influence a patient's ability to access care. Purpose: To characterize the barriers to care among rural and metropolitan-dwelling patients requiring post-discharge, HF management and their ideal model of care according to the novel CLOSE framework. Methods: We applied Clinical, LOcation and Socio-Economic status (CLOSE) profiling to a typical cohort of patients requiring post-discharge HF management following an acute admission to 4 geographically dispersed hospitals. They were then designated as proximal (<25km) or remote (≥25km) to specialist care. Patients were also designated as either independently able to access health care or facing significant barriers according the presence of 3 or more of the following factors - 1) aged >75 years, 2) living alone, 3) Non-English speaking, 4) Age adjusted Charlson Index Score of ≥5, 5) Cognitive Impairment (Montreal Cognitive Score <26) and 6) NYHA Class III/IV at discharge. These two main categories were then combined to produce 4 CLOSE groups: GROUP 1: Independent and proximal to healthcare services; GROUP 2: Independent but living remotely; GROUP 3: Barrier to health care despite living proximal to healthcare services; and GROUP 4: Barriers to healthcare and living remotely.
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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 | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".