Co-morbidity and physician use in fibromyalgia
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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 it