Small fiber neuropathy: expanding diagnosis with unsettled etiology
Bibliographic record
Abstract
PURPOSE OF REVIEW: Small fiber neuropathies (SFN) are a heterogeneous group of disorders affecting the thinly myelinated Aδ and unmyelinated C-fibers. The clinical picture is dominated by neuropathic pain, often accompanied by autonomic symptoms of variable severity. The underlying causes encompass metabolic conditions like diabetes mellitus, immuno-mediated disorders, infection, exposure to toxins, and gain-of-function variants in the genes encoding the Nav1.7, Nav1.8, and Nav1.9 sodium channel subunits, though the list of associated diseases continues to grow. Recently, increased attention has focused on immune-mediated forms, which led to the identification of potentially treatable subgroups. These discoveries have advanced our understanding of pathophysiological mechanisms. RECENT FINDINGS: Recent studies have broadened the spectrum of underlying conditions associated with SFN, including immune-mediated forms and links to SARS-CoV-2 infection and vaccines. Studies on genetic variants linked to unique clinical presentations have also yielded new insights. Furthermore, emerging perspectives highlighted disorders involving small fiber pathology that lacks typical clinical features of neuropathic pain, challenging traditional diagnostic criteria. SUMMARY: Deepening our understanding of the causes underlying SFN advances the identification of potential therapeutic targets. The clinical presentation of SFN can vary significantly and may not consistently correlate with specific underlying conditions. Therefore, a systematic investigation of possible causes through a structured diagnostic assessment is critical to unveil additional contributing factors.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| 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".