Comparison of intraepidermal nerve fiber density and confocal corneal microscopy for neuropathy
Bibliographic record
Abstract
OBJECTIVE: Compare the diagnostic characteristics of intraepidermal nerve fiber density (IENFD) and confocal corneal microscopy (CCM) for distal symmetric polyneuropathy (DSP) and small fiber neuropathy (SFN). METHODS: Participants with obesity were recruited from bariatric surgery clinics and testing was performed prior to surgery. DSP and SFN were determined using the Toronto consensus definitions of probable neuropathy. IENFD was assessed from 3 mm punch biopsies of the distal leg and proximal thigh. CCM was performed on both eyes with manual and automated counting. The Michigan Neuropathy Screening Instrument questionnaire (MNSIq) was also completed. Diagnostic capability was determined using areas under the receiver operating characteristics curve (AUC) from logistic regression. RESULTS: [6.7]). In this population, 22.9% had DSP and 14.3% had SFN. Distal leg IENFD had the largest AUC (95% confidence interval) for DSP (0.78, 0.68-0.89) and SFN (0.85, 0.75-0.96). Proximal thigh IENFD (DSP: AUC: 0.59, 0.48-0.69, SFN: AUC: 0.59, 0.46-0.73) and CCM metrics (DSP: AUC range: 0.55-0.60, SFN: AUC range: 0.45-0.62) had poorer diagnostic capability than distal leg IENFD for DSP/SFN (P < 0.05). MNSIq had similar diagnostic capability to distal leg IENFD for both DSP/SFN (DSP: AUC: 0.76, 0.68-0.85, SFN: AUC: 0.81, 0.73-0.88). More participants (52%) preferred skin biopsies to CCM. INTERPRETATION: Distal leg IENFD was the best quantitative measure of DSP/SFN. CCM had poor diagnostic characteristics and fewer patients preferred this test to IENFD. The MNSIq had similar diagnostic characteristics to distal leg IENFD, indicating its value as a diagnostic tool in the clinical setting. CLINICAL TRIAL REGISTRATION: clinicaltrials.gov: NCT03617185.
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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 | 0.004 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".