Co-morbidity and physician use in fibromyalgia
Bibliographic record
Abstract
OBJECTIVE: To describe comorbidity in women with FM, and to examine the effects of different types of comorbidity on physician use. METHODS: Women (n = 180) with primary FM were evaluated at baseline and 6 months later for self-reported health resource use and covariates. Reported comorbidity was classified into 4 categories: medical, psychiatric, "functional", and unknown. The category for "functional" conditions included disorders that have been classified by previous authors as medically unexplained symptoms such as the irritable bowel and chronic fatigue syndromes. Logistic regression models were developed to examine associations between types of comorbidity and physician use. RESULTS: Comorbid conditions were reported by over 90% of the sample. Total number of comorbid complaints was associated with high number of physician visits. In logistic regression models (controlling for age, ethnicity, education, disability, pain, and psychological vulnerability) medical comorbidity was a much stronger determinant of high number of physician visits than was "functional" comorbidity. CONCLUSIONS: Comorbidity with other disorders, both functional and medical, was high in this sample. Medical and psychiatric comorbidity were stronger determinants of high physician use than "functional" comorbidity.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.002 | 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".