Small fiber neuropathy: expanding diagnosis with unsettled etiology
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
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.
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.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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