Specific symptoms may discriminate between fibromyalgia patients with vs without objective test evidence of small-fiber polyneuropathy
Bibliographic record
Abstract
INTRODUCTION: Multiple studies now confirm that ∼40% of patients with fibromyalgia syndrome meet diagnostic criteria for small-fiber polyneuropathy (SFPN) and have objective pathologic or physiologic evidence of SFPN, whereas 60% do not. Given possibilities that tens or hundreds of millions globally could have SFPN, developing screening tools becomes important. OBJECTIVES: This analysis explored whether specific symptoms might help distinguish these fibromyalgia endophenotypes. METHODS: With institutional review board approval, all adults tested for SFPN by distal-leg skin biopsy or autonomic function testing at Massachusetts General Hospital in 2014 to 2015 were queried about symptoms. Inclusion required a physician's fibromyalgia syndrome diagnosis plus meeting the American College of Rheumatology 2010 Fibromyalgia Criteria. The primary outcome was the validated Small-fiber Symptom Survey, which captures severity of all known SFPN-associated symptoms. The Composite Autonomic Symptom Score-31, Short-Form Health Survey-36, and Short-Form McGill Pain Questionnaires provided secondary outcomes. RESULTS: = 0.019). Receiver operating characteristic analyses revealed that each item had fair diagnostic utility in predicting SFPN, with areas under the curve of 0.729. No secondary questionnaires discriminated significantly. CONCLUSION: Among patients with fibromyalgia, most symptoms overlap between those with or without confirmed SFPN. Symptoms of dysautonomia and paresthesias may help predict underlying SFPN. The reason to screen for SFPN is because-unlike fibromyalgia-its medical causes can sometimes be identified and definitively treated or cured.
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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".