The incidence of pain and its association with quality of life in patients with peritoneal dialysis
Bibliographic record
Abstract
Background The aims of this study were to investigate the incidence of pain in peritoneal dialysis (PD) patients and to analyze the correlation between pain and quality of life.Methods PD patients who followed up in our PD center from March 2016 to December 2017 were included. The Short-Form McGill Pain Questionnaire was used to assess pain status. Depression status, sleep quality, quality of life and clinical data were also collected.Results A total of 463 PD patients were included, of whom 153 patients (33.1%) with pain. The main cause of pain was calcium and phosphorus metabolism disorder (51.6%). About 101 patients (66.0%) had multiple sites of pain, and 28 patients (18.3%) with pain were treated with analgesic drugs. Binary Logistic regression analysis showed that older age (OR = 1.026; p = 0.032) and higher intact parathyroid hormone level (OR = 1.043; p = 0.040) were independent risk factors for pain in PD patients. Multivariate analysis showed that score of pain rating index was an independent risk factor for depressive symptoms (OR = 1.100; p = 0.015), the score of Pittsburgh sleep quality index (B = 0.005; p = 0.044) and the score of physical component scale (B= −0.727; p = 0.016) in PD patients.Conclusions The incidence of pain in PD patients was 33.1%. Older age and higher intact parathyroid hormone level were independent risk factors for pain. Pain was independently associated with depressive symptoms, sleep quality and quality of life in PD patients.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 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".