Identifying rehabilitation needs as part of secondary prevention in individuals with atrial fibrillation—a Delphi consensus study
Bibliographic record
Abstract
AIM: This study aimed to establish general consensus on a systematic needs assessment model to determine eligibility for cardiac rehabilitation (CR) as part of secondary prevention in individuals with atrial fibrillation (AF). Specific objectives included identifying relevant needs assessment criteria and establishing consensus on referral criteria. METHODS: A Delphi study was conducted following the ACCORD guidelines (ACcurate COnsensus Reporting Document) with participation of an international, multi-disciplinary expert panel including physicians, nurses and other healthcare professionals, across primary and secondary care as well as academic research. The panel also included six people who had AF themselves. The Delphi process involved three iterative rounds of surveys and a video meeting to determine needs assessment criteria and facilitate consensus. Data collection included qualitative feedback and quantitative voting on proposed criteria. RESULTS: Sixty-nine experts participated. There was high agreement on the importance of the study, which identified 12 needs assessment criteria related to AF symptom burden, health-related quality of life, anxiety, medicine adherence, and various risk factors. Whilst there was agreement on the needs assessment model, experts noted that referral criteria should be flexible and tailored to local healthcare settings, emphasizing that each individual's situation is unique. CONCLUSION: This Delphi study established a needs assessment model that can be adapted to local contexts for individuals with AF. More research is needed to refine referral criteria and ensure effective implementation of individually tailored CR strategies.
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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 | 0.131 | 0.095 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.005 | 0.004 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.002 | 0.010 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 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".