Non‐invasive assessment of exercise performance in children with cystic fibrosis (CF) and non‐cystic fibrosis bronchiectasis: Is there a CF specific muscle defect?
Bibliographic record
Abstract
INTRODUCTION: Peripheral muscle dysfunction is increasingly recognized as complicating respiratory disease, but this is difficult to measure non-invasively. RESEARCH QUESTION: Can skeletal muscle function and efficiency be measured during exercise non-invasively using respiratory mass spectrometry (RMS); and is the known exercise dysfunction in cystic fibrosis (CF) children related in part to a disease specific defect of skeletal muscle, or a non-specific manifestation of chronic airway infection and inflammation. METHODS: Calculations of effective pulmonary blood flow and stroke volume, blood oxygen content and oxygen dispatch from the lungs, skeletal muscle oxygen extraction and consumption, anerobic threshold and capacity, and gross, net and work efficiency in 106 controls and 36 children (18 CF) with bronchiectasis, all aged from 8 to 17 years. RESULTS: Normal values for control subjects are tabulated. CF and non-CF bronchiectatic subjects had similar physiology, and skeletal muscle abnormalities could not be detected. Reduced oxygen dispatch from the lungs, due to an inability to raise stroke volume, without an increase in functional residual capacity was the major factor in reduced exercise ability. CONCLUSIONS: Non-invasive RMS can be used to determine skeletal muscle function in children. The changes observed in CF subjects were very similar to non-CF bronchiectatic subjects and thus a CF specific defect was not demonstrated.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".