Safe and Effective Prescription of Exercise in Acute Exacerbations of Chronic Obstructive Pulmonary Disease: Rationale and Methods for an Integrated Knowledge Translation Study
Bibliographic record
Abstract
BACKGROUND: Patients hospitalized with an acute exacerbation of chronic obstructive pulmonary disease (AECOPD) engage in low levels of activity, putting them at risk for relapse and future readmissions. There is little direction for health care providers regarding the parameters for safe exercise during an AECOPD that is effective for increasing activity tolerance before discharge from hospital, especially for patients with associated comorbid conditions. OBJECTIVE: To report the rationale for and methods of a study to develop evidence-informed care recommendations that guide health care providers in the assessment, prescription, monitoring and progression of exercise for patients hospitalized with AECOPD. METHODS: The present study was a multicomponent knowledge translation project incorporating evidence from systematic reviews of exercise involving populations with chronic obstructive pulmonary disease and⁄or common comorbidities. A Delphi process was then used to obtain expert opinion from clinicians, academics and patients to identify the parameters of safe and effective exercise for patients with AECOPD. RESULTS: Clinical decision-making tool(s) for patients and practitioners supported by a detailed knowledge dissemination, implementation and evaluation framework. CONCLUSION: The present study addressed an important knowledge gap: the lack of availability of parameters to guide safe and effective exercise prescription for hospitalized patients with AECOPD, with or without comorbid conditions. In the absence of such parameters, health care professionals may adopt an 'activity as tolerated' approach, which may not improve physical activity levels in their patients. The present study synthesizes the best available evidence and expert opinion, and will generate decision-making tools for use by patients and their health care providers.
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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 | 0.306 | 0.310 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.005 |
| Bibliometrics | 0.007 | 0.006 |
| Science and technology studies | 0.002 | 0.006 |
| Scholarly communication | 0.008 | 0.009 |
| Open science | 0.004 | 0.011 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.008 | 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".