A comparison of digital retinal image quality among photographers with different levels of training using a non-mydriatic fundus camera
Bibliographic record
Abstract
PURPOSE: To evaluate the quality of digital retinal images taken by three photographers with different levels of photographic training, using a non-mydriatic fundus camera. METHODS: This study compares 45-degree digital retinal images taken with a non-mydriatic fundus camera by three different photographers with different levels of photographic training: (I) A professional ophthalmic photographer with 20 years of experience; (2) a non-professional photographer with 2 days of photographic training and experience with 50 patients; (3) a non-professional photographer with 1 hour of photographic training and experience with 10 patients. The quality of the photographs was evaluated by the consensus of two retina specialists. RESULTS: Sixty-four (64) eyes of 33 subjects were imaged by the three photographers for a total of 192 images. Thirty-four eyes were photographed in the non-dilated state. The trained ophthalmic photographer and the two non-professional photographers did not have statistically significant differences in image quality based on the image evaluations. (Chi-square P-value: 0.57). This finding was consistent for eyes in both the non-dilated and dilated state. CONCLUSIONS: Fundus image quality for images taken with a non-mydriatic camera were not significantly different among three photographers with different levels of training.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".