Multifocal electroretinogram analysis of the effectiveness of anti-VEGH treatment for clinically significant macular edema treatment
Bibliographic record
Abstract
This study aimed to compare multifocal Electroretinogram (mfERG) between before and after the treatment of clinically significant macular edema (CSME). Cross-sectional comparative study was performed on the mfERG measurements of four patients (54 ± 11 years old) diagnosed with CSME by an ophthalmologist (retina subspecialty). The subjects were examined within 1 to 2 months before and after the anti-vascular endothelial growth factor (anti-VEGF) CSME treatment. The procedure of mfERG adhered to the standard recommended by the International Society for Clinical Electrophysiology of Vision (ISCEV). Parameters included in this investigation were N1 amplitude (µV), N1 implicit time (ms), P1 amplitude (µV), and P1 implicit time (ms). Further analysis was performed by dividing the retina area into five rings zone (1st Ring, 2nd Ring, 3rd Ring, 4th Ring, and 5th Ring with a subtended surface area of 0–2°, 2–5°, 5–10°, 10–15°, and >15°, respectively). The paired sample T-test was used to compare the mfERG between before and after Anti-VEGF intervention. Overall, mean differences were observed before and after the CSME treatment with the anti-VEGF, but not statistically different for the N1 amplitude (µV), N1 implicit time (ms), P1 amplitude (µV), and P1 implicit time (ms). Further analysis results based on rings was revealed to be not statistically different, except N1 amplitude (µV) at 5 to 10° surface area (3 rd Ring), which the N1 amplitude became less negative after the treatment, -24.95 µV, (95% CI, -37.44 to -12.46), t (3) = -6.36, p < 0.05]. There was no statistically significant difference in mfERG before and after between treatment of CSME except for N1 amplitude (µV) for 3rd Ring after 1 to 2 months follow up. In the future, it is recommended that similar investigation should be conducted by involving more patients and performing a series of follow up.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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".