Evaluation of Proxy Tests for SFSN: Evidence for Mixed Small and Large Fiber Dysfunction
Bibliographic record
Abstract
BACKGROUND: Though intra-epidermal nerve fiber density (IENFD) is considered the gold standard for diagnosis of small fiber sensory neuropathy (SFSN), we aimed to determine if novel threshold values derived from standard tests of small or large fiber function could serve as diagnostic alternatives. METHODS: Seventy-four consecutive patients with painful polyneuropathy and normal nerve conduction studies (NCS) were defined as SFSN cases or controls by distal IENFD <5.4 and ≥5.4 fibers/mm, respectively. Diagnostic performance of small fiber [cooling (CDT) and heat perception (HP) thresholds, axon reflex-mediated neurogenic vasodilatation] and large fiber function tests [vibration perception thresholds (VPT) and sural nerve conduction parameters] were determined by receiver operating-characteristic (ROC) curve analyses. RESULTS: The 26(35%) SFSN cases had mean IENFD 3.3±1.7 fibers/mm and the 48(65%) controls 9.9±2.9 fibers/mm. Male gender (p = 0.02) and older age (p = 0.02) were associated with SFSN cases compared to controls. VPT were higher and CDT lower in SFSN cases, but the largest magnitude of differences was observed for sural nerve amplitude. It had the greatest area under the ROC curve (0.75) compared to all other tests (p<0.001 for all comparisons) and the optimal threshold value of ≤12 µV defined SFSN cases with 80% sensitivity and 72% specificity. CONCLUSION: In patients presenting with polyneuropathy manifestations and normal NCS, though small fiber function tests were intuitively considered the best alternative measures to predict reduced IENFD, their diagnostic performance was poor. Instead, novel threshold values within the normal range for large fiber tests should be considered as an alternative strategy to select subjects for skin biopsy in diagnostic protocols for SFSN.
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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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".