Bibliographic record
Abstract
OBJECTIVES: Despite its relative common occurrence, definitive diagnosis of small fiber neuropathy (SFN) remains problematic. In practice, patients with pain, numbness, and/or paresthesias in their lower limbs are diagnosed with SFN if found to have dissociated sensory loss in their feet, that is, impaired pinprick perception (PP) but relatively preserved vibration. We sought to assess the sensitivity and specificity of clinical examination and various diagnostic tools available for screening SFN. METHODS: Medical records of 56 patients diagnosed with SFN were reviewed. Diagnosis was based on symptoms, detailed neurological examination that included PP, and abnormal results on at least one testing modality-quantitative sudomotor axon reflex (sweat) test (QSART), quantitative sensory testing (QST), and heart rate variability (HRV) testing. RESULTS: Sensitivity of PP was relatively consistent between modalities of about 63% in presence of appropriate sensory symptoms. Laboratory testing diagnosed 88% of patients when both QSART and QST are employed. QST was most sensitive for detection of SFN with the heat-pain testing having higher sensitivity than cooling. Heart rate variability testing revealed low correlation across all groups. CONCLUSIONS: The diagnostic yield for SFN increases by combining clinical features with various testing modalities. In symptomatic patients, we propose the following diagnostic criteria for diagnosis of SFN: Definite SFN-abnormal neurological examination and both QSART and QST; Probable SFN-abnormal neurological examination, and either QSART or QST; Possible SFN-abnormal neurological exam, QSART, or QST.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".