Bibliographic record
Abstract
Aim: Pain is an important health issue that is common among patients who receive hemodialysis (HD) treatment and greatly affects the patients’ quality of life. This study aims to assess the qualitative pain characteristics of patients who receive HD treatment using the McGill Pain Questionnaire. Materials and Method: This quantitative, descriptive and cross-sectional study included 87 patients who received HD treatment. Data were collected using an information form and the McGill Pain Questionnaire in the HD clinic of a training and research hospital in Turkey between 01.04.2019 and 31.09.2019. Results: The study found that the mean current pain scores of the HD patients were moderate (2.13±0.56). The study determined that the patients experienced pain most often in the lower extremity (36.8%) and head region (29.9%) and least in the upper extremity (11.5%). The hemodialysis procedure (44.8%), not following the diet (23%), fatigue (16.1%) and stress (16.1%) were found to intensify the pain. The study found that analgesics (36.8%), resting (31%), complementary approaches (17.2%) and other practices (14.9%) relieved pain when patients were in pain. The study also found that the patients often used the words tiring (n=47), sickening (n=42), fearful (n=41) and wretched (n=38) to define the pain they felt. Conclusion: Measuring and categorizing pain are greatly important to increase the quality of life. The results obtained indicate that assessment of the pain individualistically will be a guide to provide a holistic approach in HD 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 | 0.000 | 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) | 0.002 | 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".