Pain Characteristics do not Distinguish Fibromyalgia Patients with Abnormal Skin Biopsy (P5.069)
Bibliographic record
Abstract
OBJECTIVE:To determine whether pain characteristics can distinguish between fibromyalgia patients who have abnormal skin biopsy and/or SNAP amplitudes. BACKGROUND:Patients (n=165) with fibromyalgia were assessed for differences in pain characteristics as a function of sural/medial plantar SNAP amplitudes and intra-epidermal nerve fiber densities (IENF). DESIGN/METHODS:Fibromyalgia diagnosis was verified by the American College of Rheumatology 2010 Fibromyalgia criteria. All patients underwent standard nerve conduction studies including medial plantar sensory conductions. Each patient completed a Short-Form McGill Pain Questionnaire as part of their evaluation. RESULTS:The greatest correlation existed between medial plantar SNAP amplitude and IENF density at the calf (rho=0.45; p<0.0001) although correlation also existed between sural amplitude and IENF density at the calf (rho=0.38; p<0.0001). There was no significant correlation between medial plantar or sural SNAP amplitudes and IENF density at the thigh (rho=0.10, p=0.25; rho=0.06, p=0.51). Overall pain characteristics did not distinguish either skin biopsy findings or SNAP amplitudes although some characteristics approached significance. Patients with reduced IENF at the calf had higher total pain scores than patients with normal distal IENF but this difference was not significant. The characteristic descriptions of “sharp” and “splitting” pain were most likely to distinguish these patients while characteristics of “tenderness”, “aching” and “throbbing” were least likely to be distinctive. Heaviness was significantly more often described in patients with reduced IENF at the thigh (p<0.05). Pain characteristics did not distinguish patients with preserved versus reduced SNAP amplitudes. CONCLUSIONS:Reduced IENF in patients who fulfilled the 2010 ACR criteria for FM had pain characteristics that did not clearly distinguish them from patients with normal IENF. Although a correlation existed between IENF density and SNAP amplitudes, pain characteristics did not distinguish those patients with reduced SNAPs. These data suggest that pain is nonspecific in the detection of small fiber neuropathy in fibromyalgia patients. Study Supported by:
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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 | 0.000 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| 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".