Is there benefit in referring patients with fibromyalgia to a specialist clinic?
Bibliographic record
Abstract
OBJECTIVE: To examine the benefit of specialist rheumatology consultation and followup for the first 238 patients referred to a tertiary care fibromyalgia (FM) clinic with emphasis on final diagnosis and outcome. METHODS: A retrospective chart review was performed for the first 238 patients attending a rheumatology subspecialty FM clinic. The main variables of interest were management received at the clinic, final diagnosis, and outcome. RESULTS: The final diagnosis was FM in 68%, and some other condition in the remaining 32%. Specialist contact was identified as useful in 73% of the total patient group, 96 with FM and 74 with non-FM. In the patients with FM who received followup in the clinic, outcome was judged favorable in 54%, whereas 46% showed no change or decline in health status. CONCLUSION: An important value of specialist rheumatology contact for patients with a symptom suggestive of diffuse musculoskeletal pain is to ensure that some other potentially treatable condition is not overlooked, rather than the provision of ongoing care for those with FM. Continued followup in a specialist clinic for patients with a primary diagnosis of FM is of questionable benefit.
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.
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 | 0.002 | 0.036 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".