Carbon and Travel Cost Reduction From Remote Medication Titration for Advanced Heart Failure: A Secondary Analysis of a Randomized Controlled Trial
Bibliographic record
Abstract
BACKGROUND: Optimization of guideline-directed medical therapy (GDMT) in heart failure with reduced ejection fraction (HFrEF) often requires frequent in-person visits, contributing to patient burden and health care-related carbon emissions. Remote patient management (RPM) may offer a lower carbon and cost alternative while maintaining care quality. METHODS: This is a secondary analysis of the MEDLY Titrate randomized controlled trial comparing RPM-based remote GDMT titration with usual care. Carbon emissions and patient travel costs were estimated using the Creating a Sustainable Canadian Health System in a Climate Crisis (CASCADES) Virtual Care Carbon Accounting Tool. A 1:1 nearest-neighbour matching analysis based on median 1-way travel distance was conducted to mitigate geographic confounding. Bootstrap analyses were used to estimate confidence intervals (CIs) for group differences. RESULTS: Remote optimization resulted in more patients reaching GDMT targets (98% vs 85%) and 62-day faster titration. Patients in the remote arm travelled a median of 140 km vs 213 km in usual care. Matched analysis showed significant reductions in total carbon emissions (-8.50 tonnes; 95% CI, -34.58 to -1.84), travel distance (-41,923.53 km; 95% CI, -170,491.85 to -9,064.21), and travel costs (-$12,258.78 Canadian dollars [CADs]; 95% CI, -49,806.33 to -2,661.51). A folded F test confirmed greater variance in 1-way trip distances (P < 0.001). CONCLUSIONS: Remote GDMT titration reduced travel, carbon emissions, and costs while maintaining care quality. This study is the first to quantify environmental and financial savings from remote heart failure medication titration using an integrated carbon calculator. CLINICAL TRIAL REGISTRATION: NCT04205513.
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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 | 0.004 | 0.008 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.011 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.011 | 0.001 |
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".