Bibliographic record
Abstract
Introduction: This study aimed to investigate the relationship between: the etiology of chronic renal failure (CRF), hemodialysis duration, body mass index, residual renal function (RRF), ultrafiltration volume, dialysis dose value, and frailty levels by utilizing laboratory values and patient-related data from individuals undergoing hemodialysis due to CRF.Methods: A total of 56 patients, comprising 22 females and 34 males, undergoing hemodialysis treatment due to CRF, were included in the study.Patients with active infections, hepatitis B, hepatitis C, human immunodeficiency virus, acute renal failure, or chronic liver disease were excluded.Frailty levels were assessed through a questionnaire based on the Frailty Scale and the Edmonton Frailty Scale.Results: Frailty levels were found to be higher in patients with diabetes mellitus, cardiovascular disease (CVD), and peripheral arterial disease (PAD) compared to those without these conditions (p=0.003, p=0.000, p=0.035, respectively).In patients with severe anemia (hemoglobin<10), frailty levels were also higher (p=0.024).No significant relationship was found between the patients' age, dialysis duration (in years), calcium, phosphorus, uric acid, parathyroid hormone, RRF, and the clinical frailty index averages, (p≥0.05).Conclusion: In patients undergoing hemodialysis due to CRF.Diabetes mellitus, CVD, PAD, and anemia are factors that contribute to increased frailty.In patients receiving hemodialysis for CRF, well-conducted hemodialysis treatment can be considered effective in preventing frailty.Anemia is a treatable factor, and its management may be associated with a reduction in frailty and cardiovascular mortality.
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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 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| 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".