Phase-1 Cardiac Rehabilitation in Acute Heart Failure: Development of an Early Mobilization Algorithm Through Delphi Consensus
Bibliographic record
Abstract
PURPOSE: Literature on early mobilization and exercise training in patients recovering from acute heart failure (AHF) is promising. However, there lacks uniformity in the time of initiation, exercise prescription, safety criteria, and termination criteria. Thus, the aim was to develop a mobilization algorithm for patients recovering from AHF. METHODS: A modified web-based Delphi process was undertaken involving 15 panelists from across the globe. In Round 1, new variables, modifications to suggested variables, and agreement-disagreement within the panelists were obtained. In Round 2, agreement on a 5-point Likert scale was obtained. In Round 3, the algorithm was compiled, and excluded statements were discussed via web-based calls. The new variables were grouped into themes via an inductive process. The level of agreement and rating for each statement were analyzed using descriptive statistics, including frequency and percentages. We used Kappa statistics to examine the level of agreement between the panelists for each criterion. The consensus criterion was defined a priori as statements with a mean rating of ≥4 on the 5-point Likert scale by ≥70% of the panelists and Kendall's coefficient of concordance ( W ) of ≥0.3 between panelists. RESULTS: From Round 1, 54 new variables were obtained. More than two-thirds (118/170, 69%) of statements reached consensus and reported a fair level of agreement between panelists ( W ≥ 0.3). The final algorithm with all its criteria received a 100% (13/13) consensus. CONCLUSION: Physician-referral criteria, physiotherapy assessment process, exercise prescription, safety criteria, and termination criteria were formulated for early mobilization and exercise training for patients recovering from AHF.
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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 | 0.178 | 0.118 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.006 | 0.003 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.004 | 0.006 |
| Open science | 0.004 | 0.015 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".