Assessment and management of pain in hemodialysis patients: A pilot study
Bibliographic record
Abstract
_____________________________________________________________________________________________ Purpose: To assess pain levels of hemodialysis (HD) patients and to report pain management techniques. Materials and methods: A quantitative descriptive study design with a summative approach to qualitative analysis was held, with a personal interview of the HD patients in a Southern European city hospital (n=70), using the Visual Analog Scales (VAS), the Wong-Baker Pain Scales (WBPS) and McGill Pain Questionnaire. People confused or in a coma, with hearing or reading problems and inability to communicate in the spoken language were excluded. Results: Renal patients under investigation were 69.72 ±12.45 years old, male (58.5%) and on HD for 35.5 ± 27.4 months. In the Wong Baker Scale, pain was rated as “hurts little more” 30.8%, (n=20) and in the VAS 30.8% (n=20) reported 6/10 the amount of pain experienced. Forty-six percent pinpointed internal pain in the legs. Pain experienced was characterized as sickening (70.8%), tiring (67.7%), burning (66.2%), rhythmic (86.2%), periodic (66.2%) and continuous (61.5%). The patients studied mainly manage pain either with warm towel/cloth (85.2% females and all male patients), with massage (84.2% and 88.9%, respectively) or painkillers (47.4% and 52.6%, respectively). In a correlation of gender and pain management techniques, statistical significance was found only with warm towel (p=0.038). Conclusions: As renal patients are an increasing group of healthcare service users, and pain is affecting their everyday life, it is essential to individualize pain evaluation and to provide further education to clinical nurses so that they can effectively manage pain.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".