Psychiatric Disorders in Young Adults Diagnosed with Juvenile Fibromyalgia in Adolescence
Bibliographic record
Abstract
OBJECTIVE: Adolescents with juvenile-onset fibromyalgia (JFM) have increased rates of psychiatric disorders, but to our knowledge no studies have examined psychiatric disorders in adolescents with JFM when they enter young adulthood. This study examined the prevalence of psychiatric disorders in young adults diagnosed with JFM during adolescence and the relationship between mental health diagnoses and physical functioning. METHODS: Ninety-one young adults (mean age 21.60, SD 1.96) with a history of JFM being followed as part of a prospective longitudinal study and 30 matched healthy controls (mean age 21.57, SD 1.55) completed a structured interview of psychiatric diagnoses and a self-report measure of physical impairment. RESULTS: Young adults with a history of JFM were more likely to have current and lifetime histories of anxiety disorders (70.3% and 76.9%, respectively) compared with controls (33.3% for both, both p < 0.001). Individuals with JFM were also more likely to have current and lifetime histories of major mood disorders (29.7% and 76.9%, respectively) compared with controls (10% and 40%, p < 0.05). The presence of a current major mood disorder was significantly related to impairment in physical functioning [F (1, 89) = 8.30, p < 0.01] and role limitations attributable to a physical condition [F (1, 89) = 7.09, p < 0.01]. CONCLUSION: Psychiatric disorders are prevalent in young adulthood for individuals with a history of JFM, and a current major mood disorder is associated with greater physical impairment. Greater attention to early identification and treatment of mood disorders in patients with JFM is warranted.
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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 | 0.001 | 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 | 0.001 |
| 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".