Fibromyalgia syndrome in <scp>T</scp>urkish hemodialysis patients
Bibliographic record
Abstract
The aim of our study was to evaluate the frequency of fibromyalgia syndrome (FMS) in hemodialysis (HD) patients and to assess whether this syndrome is associated with gender, age, duration of HD, or various laboratory parameters. This study was composed of 221 chronic HD patients (99 females and 122 males), and we recorded each participant's age, gender, causes of kidney failure, HD duration, education level, and symptoms related to FMS, which was diagnosed according to the 2010 American College of Rheumatology criteria. We documented the laboratory parameters for all patients. In addition, patients with FMS filled out the Fibromyalgia Impact Questionnaire. Twenty-two patients met the diagnostic criteria for FMS (9%), and there were no statistically significant differences related to age, gender, or HD duration between FMS and non-FMS groups (P > 0.05). In addition, the education levels were lower in patients diagnosed with FMS (P < 0.05), and there were statistically significant differences related to sleep disturbance, fatigue, and cognitive symptoms between the two groups (P < 0.05) as well. However, their laboratory parameters were similar (P > 0.05). There was a higher prevalence of FMS in HD patients than in the general population. Sleep disturbances, fatigue, education level, and cognitive symptoms were associated with FMS, but there was no correlation between the laboratory parameters and this condition.
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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".