311 Usefulness of 2016 ACR criteria for diagnosis of fibromyalgia and its prevalence in autoimmune rheumatological conditions
Bibliographic record
Abstract
Background: Fibromyalgia is a common cause of chronic widespread musculoskeletal pain, often accompanied by fatigue, cognitive disturbance and multiple somatic symptoms. It is the most common cause of generalised, musculoskeletal pain in women between the ages of 20 and 55 years. The prevalence of fibromyalgia is approximately 2 to 5 percent in the general population worldwide. The prevalence of fibromyalgia is reported to be higher (up to 15.4%) in rheumatology population. Fibromyalgia can co-exist with other rheumatological diseases, and its recognition is important for the optimal management of these conditions. Methods: The aim of present study was to assess the usefulness of 2016 ACR Fibromyalgia criteria in diagnosis of fibromyalgia in outpatient clinic setting. We also wanted to ascertain the prevalence of fibromyalgia in autoimmune inflammatory diseases.The study was conducted at a district general hospital in Essex, UK. All consecutive patients attending rheumatology outpatients with autoimmune rheumatological illnesses- Inflammatory arthritis (IA), Connective tissue diseases (CTD) - and primary fibromyalgia were included in the study. They were asked to fill the questionnaire based on 2016 ACR Fibromyalgia criteria.
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 | 0.004 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".