Specific symptoms may discriminate between fibromyalgia patients with vs without objective test evidence of small-fiber polyneuropathy
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it