Comparison of quadriceps muscle size and quality in adults with cystic fibrosis with different severities of cystic fibrosis transmembrane conductance regulator protein dysfunction
Bibliographic record
Abstract
BACKGROUND: Cystic fibrosis (CF) is characterized by CF transmembrane conductance regulator (CFTR) dysfunction. CFTR protein is expressed in human skeletal muscle; however, its impact on skeletal muscle is unknown. The objectives of this study were to compare quadriceps muscle size and quality between adults with various severities of CFTR protein dysfunction. METHODS: We conducted a prospective, cross-sectional study comparing 34 adults with severe versus 18 with mild CFTR protein dysfunction, recruited from a specialized CF centre. Ultrasound images of rectus femoris cross-sectional area (RF-CSA) and quadriceps layer thickness for muscle size, and rectus femoris echogenicity (RF-ECHO) (muscle quality) were obtained. Multivariable linear regression models were developed using purposeful selection technique. RESULTS: colonization. However, a sensitivity analysis indicated that the result was influenced by the specific confounders being adjusted for in the model. We did not find any significant differences in quadriceps layer thickness or RF-ECHO between the two groups. CONCLUSION: We found no differential impact of the extent of diminished CFTR protein activity on quadriceps muscle size or quality in our study cohort. Based on these findings, CFTR mutation status cannot be used differentiate leg muscle size or quality in people with CF.
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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 | 0.001 | 0.003 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".