Diagnostic confounders of chronic widespread pain: not always fibromyalgia
Bibliographic record
Abstract
INTRODUCTION: Chronic widespread pain (CWP) is the defining feature of fibromyalgia (FM), a worldwide prevalent condition. Chronic widespread pain is, however, not pathognomonic of FM, and other conditions may present similarly with CWP, requiring consideration of a differential diagnosis. OBJECTIVES: To conduct a literature search to identify medical conditions that may mimic FM and have highlighted features that may differentiate these various conditions from FM. METHODS: A comprehensive literature search from 1990 through September 2016 was conducted to identify conditions characterized by CWP. RESULTS: Conditions that may mimic FM may be categorized as musculoskeletal, neurological, endocrine/metabolic, psychiatric/psychological, and medication related. Characteristics pertaining to the most commonly identified confounding diagnoses within each category are discussed; clues to enable clinical differentiation from FM are presented; and steps towards a diagnostic algorithm for mimicking conditions are presented. CONCLUSION: Although the most likely reason for a complaint of CWP is FM, this pain complaint can be a harbinger of illness other than FM, prompting consideration of a differential diagnosis. This review should sensitize physicians to a broad spectrum of conditions that can mimic FM.
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 | 0.001 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".