Test–retest reliability of intra-epidermal electrically evoked potentials in comparison with other modalities and across stimulation intensities
Bibliographic record
Abstract
Intra-epidermal electrically evoked potentials (IEEPs) might represent a promising method for an improved characterization of certain spinal pathologies. Before successful clinical implementation, investigating IEEPs reliability is a prerequisite. This study aimed to assess the test-retest reliability of IEEPs compared to contact heat (CHEPs) and pinprick (PEPs) evoked potentials (Experiment 1) and across different intra-epidermal electrical stimulation (IES) intensities (Experiment 2). Experiment 1 included 26 participants (12f, 25.3 ± 4.6years) and assessed pain-related evoked potentials (PREPs) following contact heat (35-60 °C), pinprick (256 mN), and IES (2 × electrical detection threshold, EDT) to the volar forearm. Experiment 2 included 30 participants (20f, 27.7 ± 3.7years) and assessed IEEPs at four IES intensities (1.5 ×, 2 ×, 4 × EDT, and 0.5 mA). Both experiments assessed test-retest reliability with intraclass correlation coefficients (ICCs) and Bland-Altman analyses for N-latencies, NP-amplitudes, and pain ratings. While IEEPs at 2 × EDT in Experiment 1 showed "excellent" reliability for NP-amplitude, comparable to CHEPs and PEPs, reliability for N-latency and pain ratings ranged from "poor" to "fair". This reliability of IEEP N-latencies and pain ratings improved to "good" and "excellent" by applying higher intensities of IES such as 4 × EDT and 0.5 mA. Given the high reliability of IEEPs at 4 × EDT and 0.5 mA, these intensities may be recommendable for clinical application.
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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 | 0.009 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".