The capability of electroretinograms to detect a reduced detection of photons by photoreceptors
Bibliographic record
Abstract
The electroretinogram (ERG) is an objective measurement of the electrical response of retinal neurons, including photoreceptors, to light. The basic function of photoreceptors is to convert photons into neural signals. The present study investigated the capability of different ERG techniques to detect a reduced number of photons detected by photoreceptors compared to a functional assessment of the photon noise, which quantifies the number of photons detected by photoreceptors. The number of photons detected by photoreceptors were artificially reduced using a neutral density filter of 0.6, which reduced light intensity by a factor of 4. Three ERG techniques (full-field, pattern and multifocal) were performed for the measurement of photoreceptor electrical responses under a baseline and a reduced light intensity (i.e., neutral density filter) condition. The latency and amplitude of different retinogram waves were analyzed (full-field: a, b, flicker 30hz; pattern: P1 of each of the 5 rings; multifocal: P50, N95). The photon noise was derived from two contrast sensitivity measurements under specific conditions (presence and absence of noise, 0.5 cycles per degree, 2 Hz) using a motion direction discrimination task. The capability of each measurement (photon noise and various amplitudes and latencies of ERG techniques) to discriminate the baseline and reduced light intensity conditions was quantified using a ROC analysis. The results showed that no ERG parameter was more effective than the photon noise in discriminating between the baseline and reduced light intensity conditions (area under the ROC curve was 0.96 for the photon noise and ranged up to 0.84 for the various ERG parameters). We found no evidence that any ERG parameter would be more useful than the functional measurement of photon noise in detecting a reduced number of photons detected by photoreceptors.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".