MO866: Evaluating Psychosocial Contributions to Musculoskeletal Disorders in Hemodialysis Patients: A Single Center Experience From Egypt
Bibliographic record
Abstract
Abstract BACKGROUND AND AIMS Patients with end-stage kidney disease (ESKD) on regular hemodialysis (HD) suffer from a high burden of comorbidities. Musculoskeletal disorders (MSDs) are frequently encountered in those patients. The factors linked to MSDs in HD patients are incompletely defined, therefore this study was done to figure out how often musculoskeletal symptoms are in those patients and identify their association with many psychosocial aspects including quality of sleep, social support and fatigue. METHOD This cross-sectional study was carried out in the HD unit at Mansoura University Hospital, Egypt in the period from August to December 2021. The sample included 94 ESRD patients on regular HD for more than 3 months. Sociodemographic characteristics, clinical and therapeutic data were collected. The Nordic Musculoskeletal Questionnaire (NMQ-E) was used to determine the prevalence and patterns of MSDs in various parts of the body areas. Moreover, the modified Edmonton Symptom Assessment System, multidimensional Fatigue Inventory (MFI-20), Pittsburgh Sleep Quality Index (PSQI), Perceived Social Support from Family Scales were completed by the patients. MSDs predictors were evaluated by univariate regression analysis. RESULTS The mean age of the studied patients was 49.73 years and more than half of them (59.6%) were males. MSDs affected nearly three quarters (72.3%) of patients. The most frequently encountered MSD domains were knee pain (48.9%), low back pain (43.6%), shoulder pain (41.6%), hip/thigh pain (35.1%) and neck pain (35.1%) (Figure 1). Regarding Pain and symptom burden, patients with MSDs had significantly higher scores of pain (P < 0.001), fatigue (P = 0.01), depression (P = 0.015) and anxiety (P = 0.003). Regarding MFI scale, patients with MSDs experienced reduced activity (P = 0.02). Subjective sleep quality, daytime dysfunction domains and global PSQI score were worse among patients with MSDs (P = 0.02, 0.031 and 0.036, respectively). Patients with MSDs perceived less social support (P = 0.029). Female gender (P = 0.013), higher scores of fatigue (P = 0.012), depression (P = 0.014), anxiety (P = 0.004), reduced activity (P = 0.029) domains and PSQI score (0.027) were the significant predictors of MSDs in the studied HD patients (Table 1). CONCLUSION MSDs appears to frequently occur in HD patients. Female gender, fatigue, depression, anxiety, reduced activity and poor sleep quality can predict the occurrence of MSDs in HD patients.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".