Ultrasound quadriceps muscle thickness is variably associated with frailty in haemodialysis recipients
Bibliographic record
Abstract
BACKGROUND: Ultrasonographic quantitation of quadriceps muscle mass is increasingly used for assessment of sarcopenia, but its relationship with frailty in haemodialysis recipients is not known. This study explores the relationship between ultrasound-derived bilateral anterior thigh thickness (BATT), sarcopenia, and frailty by common frailty tools (Frailty Phenotype [FP], Frailty Index [FI], Edmonton Frailty [EFS], and Clinical Frailty Scale [CFS]). METHODS: This was an exploratory analysis of a subgroup of adult prevalent (≥3 months) haemodialysis recipients deeply phenotyped for frailty. Ultrasound assessment of BATT was obtained with participants at an angle of ≤45°, with legs outstretched and knees resting at 10°-20°, according to an established protocol. Associations with frailty were explored via both linear and logistic regressions for BATT, Low Muscle Mass (LMM), and sarcopenia with stepwise adjustment for a priori covariables. RESULTS: In total 223 study participants had ultrasound measurements. Frailty ranged from 34% for FP to 58% for FI. BATT was associated with increasing frailty on simple linear regression by all frailty tools, but lost significance on addition of covariables. Upon dichotomising frailty tools into Frail/Not Frail, BATT was associated with frailty by all tools on univariable analyses, but only retained association for EFS on the fully adjusted model (OR 0.97, 95% C.I. 0.94-1.00, P = 0.05). CONCLUSIONS: Ultrasound measures of quadriceps thickness is variably associated with frailty in prevalent haemodialysis recipients, dependent upon the frailty tool used, but not independent of other variables. Further work is required to establish the added value of sarcopenia measurement in frail haemodialysis patients. TRIAL REGISTRATION: Clinicaltrials.gov : NCT03071107 registered 06/03/2017.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| 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 | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".