Correlation between body composition analysis and cognition in maintenance hemodialysis patients
Bibliographic record
Abstract
ObjectiveTo employ body composition monitor （BCM） to measure body composition in patients with maintenance hemodialysis （MHD） and examine the correlation between measurement results and cognitive function.MethodsFrom May 2019 to December 2019，a total of 106 patients with chronic renal failure （CRF） receiving MHD were selected. Their general clinical data were collected. Various parameters of body composition were analyzed by BCM，including overhydration （OH），fat tissue index （FTI），lean tissue index （LTI），body mass index （BMI），total body water （TBW），extracellular water （ECW） and intracellular water （ICW）. Montreal Cognitive Assessment Scale （MoCA） was employed for assessing cognitive function. Based upon the cognitive function score，they were divided into two groups of normal cognitive function and cognitive dysfunction group. And correlation between body composition and cognitive function was examined.ResultsSignificant inter-group differences existed in age，duration of dialysis，OH/ECW，ECW/H，LTI and ICW （P<0.05）. Multiple linear regression analysis indicated that age （P=0.007），OH/ECW （P=0.044），ECW/H （P=0.001） and LTI （P=0.000） were correlated with cognitive impairment.ConclusionAge，OH/ECW，ECW/H and LTI are significantly associated with cognitive dysfunction in MHD patients. Advanced age，higher levels of OH/ECW and ECW/H and lower level of LTI were accompanied with worse cognition.
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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 | 0.000 | 0.001 |
| 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 | 0.000 | 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".