Fibromyalgia: Its prevalence and impact on the quality of life on a hemodialyzed population
Bibliographic record
Abstract
Fibromyalgia syndrome (FMS) is characterized by widespread musculoskeletal pain. It has negative effects on quality of life and has been poorly investigated in specific populations. Our aim was to determine the prevalence of FMS in Brazilian hemodialysis (HD) patients and to investigate its effects on the quality of life. We investigated 311 patients on HD who were submitted to physical examination towards the classification of FMS. All subjects from FMS and control groups were submitted to laboratorial investigation and completed questionnaires of quality of life. The prevalence of FMS was 3.9%, which was close to that of the general population. Most patients were females and from non-Caucasian races. No difference between FMS and control groups was observed regarding race, dialysis adequacy, nutritional status and level of schooling. Ionized calcium was higher in the FMS group than in the control group. There was no association between FMS and secondary hyperparathyroidism. On the other hand, FMS was associated with worse quality of life, depression and anxiety. In conclusion, the prevalence of FMS in HD patients was similar to that of the general population. It was associated with decreasing quality of life in HD patients, in addition to higher degrees of depression and anxiety. No laboratory tests could identify FMS patients on HD. Fibromyalgia syndrome subsequently follows without a well-established mechanism of pathogenesis, and seems to be due to multifactorial causes. Its true impact on the quality of life of HD patients deserves more attention by nephrologists.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.001 | 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".