Ultrasound diaphragm activity in cystic fibrosis: a normative study
Bibliographic record
Abstract
Introduction: In COPD and critical illnesses, diaphragm ultrasound is a marker of disease severity and clinical outcomes. We report on quantitative values of diaphragmatic ultrasound variables and its predictors in the CF population, which are currently lacking. Methods: CF patients were prospectively recruited. Diaphragm ultrasound was performed and compared to lung function tests, handgrip strength, fat-free mass (FFM), transthyretin, vitamin A, E and D levels, C-reactive protein (CRP), dyspnea levels and rate of acute exacerbation (AE). Diaphragm activity was reported as thickening fraction during maximal inspiration (TFmax, maximal contractile action) and TF%max (ratio of TF during tidal breathing and TFmax, representing contractile requirement of tidal breathing). Results: 110 patients were included [61 males, median (interquartile range) age 31 (27-38) years and FEV1 66 (46-82)% predicted]. Median TFmax was 86 (55-126)% and its lower 5th percentile was 30%. TF%max was not correlated to age, FFM or vitamin levels, but significantly correlated to transthyretin (rho=-0.27, p=0.005), CRP (rho=0.47, <0.001), FEV1 and handgrip strength (both p<0.05). TF%max was significantly higher in patients with >2 AE/year (44±25 vs 29±17, p=0.001), in those with mMRC score >2 (69±15 vs 31±18%, p<0.001) and was a better marker of dyspnea level than FEV1 (AUROC 0.87 vs 0.77, p=0.03). Conclusion: In CF patients, the lower limit of normal for TFmax is 30%. TF%max is related to peripheral strength, markers of systemic inflammation, nutritional status and FEV1, and outperforms FEV1 as a marker of dyspnea levels. This lays the basis for studies evaluating diaphragmatic activity as a marker of prognosis and efficacity of interventions in 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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".