Compliance, quality of life, and contributing factors in renal transplantation waiting list patients
Bibliographic record
Abstract
Poor patient compliance is common during dialysis therapy. We aimed to study incidence of noncompliance, contributing factors, and effects on quality of life (QOL) among cadaveric renal transplantation waiting list patients. We included 86 renal transplantation waiting list patients (56M/30F). Dialysis duration, previous renal transplantation history, comorbid conditions, interdialytic weight gain, predialysis BUN, creatinine, potassium, and phosphate were recorded. Noncompliance criteria were skipping >1 dialysis session or shortening a dialysis session>10 min in 1 month, interdialytic weight gain>5.7% of body weight, predialysis serum potassium >6 mEq/L, and phosphate level >7.5 mg/dl. There were 49 noncompliant (age: 46.8 ± 21.8 years, HD duration: 83.9 ± 48.7 months) and 37 compliant (age: 42.8 ± 12.1 years, HD duration: 96.5 ± 45.2 months) patients. QOL was evaluated by short form 36 and depression levels by Beck Depression Inventory. Previous renal transplantation was present in 24.4% and comorbid diseases in 31.3% of all patients. In depressed patients, 77.8% had comorbid diseases. No difference was found between the groups considering age, gender, dialysis duration, previous transplantation history, and comorbid diseases (p > 0.05). Noncompliant patients had lower QOL (p < 0.04). Noncompliant patients had higher degree of depression (p = 0.01). QOL and Beck scores were negatively correlated (p = 0.001, r = −0.561). Noncompliance to diet and dialysis therapy is associated with depression, which further decreases QOL in renal transplantation waiting list patients. Early diagnosis of depression, is possible by monitoring noncompliance, and therapeutic intervention may benefit during the transplantation‐waiting period.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".