Dog ownership status and self‐assessed health, life‐style and habitual physical activity in chronic hemodialysis patients
Bibliographic record
Abstract
Introduction The Statement from the American Heart Association has emphasized a need for novel strategies that can reduce the risk of cardiovascular disease (CVD). Taking a dog for a walk forces its owner to take physical activity. Several studies have explored the relationship between pet ownership and CVD and most reported benefits. This issue has not been investigated in dialysis patients. The aim of the study was to analyze the influence of pet ownership on health and physical activity in hemodialysis patients. Methods 270 chronic hemodialysis patients (172 male, 98 female, mean age 62.7 ± 14.0 years, hemodialysis vintage 4 ± 5 years) took part in the survey focused on their general health and physical activity. Findings Two hundred nineteen (81%) patients were mobile. One hundred sixteen participants had dog at home (43%). An additional physical activity was reported by 46 dog owners (40%) compared with 34 (23%) of nonowners (P = 0.002). Patients who often go for a walk were more often pet owners 49 (57%) than nonowners (n = 37, 43%; P = 0.004). Pet owners were younger (58.3 ± 13.6 vs. 66 ± 13.5 years). Body mass index (BMI) was similar. Patients with BMI from upper tertile (>27.5 kg/m(2) ) and from lower (<23.9) were more often dog owners than from the middle (52.9%, 43.7%, and 31.4%). Dog owners were on dialysis for longer time (5.0 ± 6.5 vs. 3.5 ± 3.7 years; P = 0.02). Discussion Dog ownership appears to positively influence the level of physical activity. Age but not time on dialysis seems to be the most important factor that influences a decision to own a pet and undertake physical activity.
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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 | 0.000 | 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) | 0.002 | 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".