Ultrasound-assessed diaphragm dysfunction predicts clinical outcomes in hemodialysis patients
Bibliographic record
Abstract
Skeletal muscle atrophy is prevalent and remarkably increases the risk of cardiovascular (CV) events and mortality in hemodialysis (HD) patients. However, whether diaphragm dysfunction predicts clinical outcomes in HD patients is unknown. This was a prospective cohort study of 103 HD patients. After assessment of diaphragm function by ultrasonography and collection of other baseline data, a 36-month follow-up was then initiated. Participants were divided into diaphragm dysfunction (DD+) group and normal diaphragm function (DD-) group, according to cutoff value of thickening ratio (i.e. the change ratio of diaphragm thickness) at force respiration. The primary endpoint was the first nonfatal CV event or all-cause mortality. A secondary endpoint was less serious CV events (LSCEs, a composite of heart failure readmission, cardiac arrhythmia or myocardial ischemia needed pharmacological intervention in hospital). 98 patients were eligible to analysis and 57 (58.16%) were men. 28 of 44 patients(63.64%) in DD+ group and 23 of 54 patients (42.59%) in DD- group had at least one nonfatal CV event or death (p = 0.038). Compared to DD- group, DD+ group had significantly higher incidence of LSCEs (21 vs.14, p = 0.025) and shorter survival time (22.02 ± 12.98 months vs. 26.74 ± 12.59 months, p = 0.046). Kaplan-Meier analysis revealed significantly higher risks of primary endpoint (p = 0.039), and LSCEs (p = 0.040) in DD+ group. Multivariate hazard analysis showed that DD+ group had significantly higher risk of primary endpoint [hazard ratio (HR) 1.59; 95% confident interval (CI) 1.54-1.63], and LSCEs (HR 1.47; 95%CI 1.40-1.55). Ultrasound-assessed diaphragm dysfunction predicts clinical outcomes in HD patients.Trial registration: This study was registered with Chinese Clinical Trials Registry ( www.chictr.org.cn ) as ChiCTR1800016500 on Jun 05, 2018.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".