Incidence and risk factors of falls in patients undergoing hemodialysis: A multicenter survey in northern China
Bibliographic record
Abstract
INTRODUCTION: Patients undergoing hemodialysis (HD) are at a higher risk of falls than healthy individuals. Further knowledge regarding the risk of falls could lead to better risk prevention strategies. We designed a multicenter, prospective cohort study according to the strengthening of the reporting of observational studies in epidemiology (STROBE) guidelines to investigate the incidence and risk factors of falls in patients undergoing hemodialysis in Northern China. METHODS: Patients undergoing hemodialysis in six hemodialysis units were recruited from January 2019 to January 2020. Data on demographics and disease conditions were collected at baseline. Data on other variables, the incidence of falls, and related conditions were collected every 3 months during a 1-year follow-up. The Generalized Estimating Equation model was used to evaluate factors associated with falls. FINDINGS: This study included 472 patients. The incidence of falls was 0.31 per patient year. In patients aged 45-64 years (p = 0.01; odds ratio [OR]: 14.801; 95% confidence interval [CI]: 1.897-115.453) and ≥ 65 years (p = 0.007; OR: 16.562; 95% CI: 2.118-129.521), anemia (p = 0.015; OR: 2.122; 95% CI: 1.154-3.902) and moderately (p = 0.003; OR: 5.439; 95% CI: 1.791-16.516) and severely abnormal timed up and go test (TUGT) levels (p = 0.001; OR: 7.032; 95% CI: 2.226-22.216) were identified as independent risk factors of falls. DISCUSSION: Falls are prevalent among patients undergoing in-center hemodialysis. Advanced age, anemia, and moderately and severely abnormal TUGT levels may be risk factors of falls.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".