Bibliographic record
Abstract
Cystic fibrosis (CF) is a multisystem disease impacting organs beyond the lungs, including the pancreas, gastrointestinal tract, liver, sweat glands, reproductive organs, and skeletal muscle. 2 , 3 Advances in CF therapy, including inhaled antibiotics, mucolytic therapies, nutritional support, lung transplantation, and most recently CF transmembrane conductance regulator (CFTR) modulator drugs have increased the median age of survival for Canadians with CF from 31.9 years in 1990 to 55.4 years in 2020.4 , 5 This shift towards more adults ageing with CF will potentially be accompanied by the presence of various cardiometabolic and musculoskeletal comorbidities in people with CF.In Canada, CF is managed by specialized interdisciplinary paediatric and adult centres, and in 2020 there were more than 18,000 CF clinic visits.5 Malik and colleagues administered an online survey to examine current physiotherapy practices, barriers, or facilitators of these CF centres in regard to exercise testing and training.1 Both aerobic and resistance testing and training were underutilized, which aligns with previous findings at other international CF centres.Understanding the rationale for exercise testing and training in the care of CF patients is important.A clear use case is people with end-stage respiratory disease who undergo routine exercise assessment for several reasons including supplemental oxygen prescription, determining lung transplant eligibility, and monitoring exercise training and exercise capacity during pre-habilitation, and early post-operative rehabilitation.The rationale for exercise assessment and training in non-transplant populations to establish a baseline functional level, track the efficacy of medical and rehabilitative therapies, and prescribe exercise may vary.Recently Gruet and colleagues advocated for rethinking exercise in CF, specifically in regard to adapting training concepts and strategies to an ageing CF population with associated comorbidities.6 While treatment for the paediatric CF population focuses on the development of motor skills and neuromuscular fitness to normalize physical activity participation from a young age and at an early stage of CF, adults with CF require approaches that will facilitate long-term maintenance of habitual physical activity and exercise, particularly in the new era of disease-modifying CFTR modulator therapies.Although the long-term impact of CFTR modulators are not known
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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 | 0.005 | 0.050 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.005 | 0.004 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.006 | 0.003 |
| Research integrity | 0.044 | 0.044 |
| Insufficient payload (model declined to judge) | 0.024 | 0.020 |
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".