Physical function was related to mortality in patients with chronic kidney disease and dialysis
Bibliographic record
Abstract
Previous studies have shown that exercise improves aerobic capacity, muscular functioning, cardiovascular function, walking capacity, and health-related quality of life (QOL) in patients with chronic kidney disease (CKD) and dialysis. Recently, additional studies have shown that higher physical activity contributes to survival and decreased mortality as well as physical function and QOL in patients with CKD and dialysis. Herein, we review the evidence that physical function and physical activity play an important role in mortality for patients with CKD and dialysis. During November 2016, Medline and Web of Science databases were searched for published English medical reports (without a time limit) using the terms "CKD" or "dialysis" and "mortality" in conjunction with "exercise capacity," "muscle strength," "activities of daily living (ADL)," "physical activity," and "exercise." Numerous studies suggest that higher exercise capacity, muscle strength, ADL, and physical activity contribute to lower mortality in patients with CKD and dialysis. Physical function is associated with mortality in patients with CKD and dialysis. Increasing physical function may decrease the mortality rate of patients with CKD and dialysis. Physicians and medical staff should recognize the importance of physical function in CKD and dialysis. In addition, exercise is associated with reduced mortality among patients with CKD and dialysis.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".