Development of three-step holistic care pathways to detect and manage comorbidities in patients with atrial fibrillation: the Horizon 2020 EHRA-PATHS consortium
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Aims: Older patients with AF (≥65 years) have on average four additional comorbidities. Comorbidity management requires a systematic approach for identification, and interdisciplinary care, often lacking in clinical practice. The EHRA-PATHS project's overall aim is to create an approach to systematically address multimorbidity in older patients with AF. Methods and results: This project involves a consortium of 14 partners from 11 European countries. The comorbidity care pathways were developed using a stepwise approach. (i) A literature study. (ii) Online meetings/discussions to create structured care pathways. (iii) A two-round Delphi study for consensus on the final pathways (agreement ≥80%) and to rank the comorbidities for priority. (iv) Selection of comorbidities for evaluation in the planned randomized controlled trial (RCT). Development of care pathways for 23 comorbidities or special clinical settings was obtained and agreed upon. The Delphi surveys were sent to 37 consortium experts. After round 1 (28 responses), 13 pathways reached an agreement ≥80%. Twelve adjusted pathways were presented in round 2 (27 responses), of which 8 received an agreement ≥80%. The last four pathways were finalized after expert consensus. Hypertension, heart failure, and overweight were ranked as the most important comorbidities. Conclusion: A structured process of expert meetings and two Delphi rounds led to the development and ranking of 23 concise care pathways to identify and manage comorbidities in patients with AF. All pathways will be combined into a software tool, providing clinicians with a systematic approach to comorbidity management, which will be tested in the RCT of EHRA-PATHS.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it