Fibromyalgia syndrome: under-, over- and misdiagnosis.
Bibliographic record
Abstract
Fibromyalgia syndrome (FM) is an enigma. During the past three decades, with the gradual acceptance of the validity of FM, it is variously under-, over and misdiagnosed. Evidence-based interdisciplinary guidelines have suggested a comprehensive clinical assessment to avoid this diagnostic conundrum. Every patient with chronic pain should be screened for chronic widespread pain (pain in four of five body regions) (CWP). Those with CWP should be screened for presence of additional major symptoms of FM: unrefreshed sleep and fatigue. A complete medical (including drug) history and complete physical examination is mandatory in the evaluation of a patient with CWP in order to consolidate the diagnosis of FM or identify features that may point to some other condition that may have a presentation similar to FM. Limited simple laboratory testing is recommended to screen for possible other diseases. The 2016 criteria may be used to further confirm the clinical diagnosis of FM. In consideration of the differential diagnosis of FM, attention should be paid to the presence of other chronic overlapping pain conditions and of mental disorders. FM as a stand alone diagnosis is however rare, as most patients with FM meet criteria for other chronic overlapping pain conditions or mental disorders. The severity of FM should be assessed in order to direct treatment approaches and help inform the likely outcome for an individual patient.
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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 | 0.004 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".