Bibliographic record
Abstract
OBJECTIVES: This study aims to assess the relationship between fibromyalgia syndrome (FMS) and vitiligo in Iraqi patients and evaluate the predictors of this relationship, if present. PATIENTS AND METHODS: The case-control study included 100 Iraqi patients (46 males, 54 females; mean age 30.4±14 years; range 15 to 65 years) with vitiligo and 200 age- and sex-matched healthy controls (74 males, 126 females; mean age 30.3±9.4 years; range 15 to 62 years). Baseline characteristics of patients and controls were recorded. The 2012 Canadian Guidelines criteria were used for the diagnosis of FMS and applied to all patients and controls. RESULTS: Prevalence of FMS in vitiligo patients and controls was 12% and 7%, respectively (p=0.15, odds ratio=1.8, 95% confidence interval=0.8-4.08). FMS symptoms in vitiligo patients were fatigue (46%), diffuse body pain (34%), sleep disturbance (33%), cognitive dysfunction (30%), and mood disorders (23%), while visceral involvements were central nervous system (52%), skin (35%), gastrointestinal tract (32%), cardiovascular system- respiratory system (16%), genitourinary tract (8%), and ear nose throat (7%). Of vitiligo patients, FMS was significantly more common among females (22.2%) compared to none among males (0%) (p<0.05). Prevalence of FMS was restricted to female sex only and a significantly higher prevalence rate of FMS was found among female vitiligo patients (22.2%) compared to controls (9.5%). Receiving phototherapy significantly increased the risk of having FMS by 5 times compared to female patients not receiving phototherapy. Use of any steroid reduced the risk of having FMS by 2.5 times (inverse of odds ratio=0.4) among females patients (p>0/05). No significant association was found between FMS in vitiligo patients and age, disease duration, type of vitiligo, use of any immunosuppressant and body mass index (p>0.05). CONCLUSION: Fibromyalgia syndrome was more prevalent in vitiligo patients compared to controls, which was clinically important but statistically not significant. There was a significant association between FMS in vitiligo patients and female sex, severe form of vitiligo, and receiving phototherapy. This may suggest that early diagnosis of FMS in vitiligo patients may help in early treatment and subsequently improve patients' quality of life.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".