Comparing the RETeval® portable ERG device with more traditional tabletop ERG systems in normal subjects and selected retinopathies
Bibliographic record
Abstract
Abstract Purpose Our study aimed to determine if ISCEV standard-like ERGs recorded with the LKC RETeval® portable ERG unit compared to those obtained using the more traditional tabletop unit. Methods ERGs recorded from normal subjects and patients affected with retinal ON and OFF pathway anomalies were compared. Analysis included peak time and amplitude measurements as well as time–frequency domain analysis with the discrete wavelet transform of waveforms obtained with the two systems. Results Although both systems were similarly able to record reliable and highly reproducible ERG responses, there were major discrepancies in ERG responses between the portable and tabletop units, pointing toward a weaker stimulation of the retinal OFF pathway with the portable RETeval® unit. Conclusion The portable RETeval® unit appears to be able to record highly reproducible and diagnostically useful clinical ERGs, albeit with some significant differences in waveform composition compared to those obtained with more standard tabletop systems. Given the unknown origin of these waveform discrepancies, if left uncorrected, these differences could potentially lead to erroneous interpretation when used in the clinical context and/or compared to ERGs recorded using more traditional table top units. Clearly, more research is warranted before handheld devices, such as the RETeval®, can be homologated as a diagnostically sound ERG devices.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".