Drusen classification and quantification in pseudophakic postmortem eyes implanted with or without blue-light filtering intraocular lenses
Bibliographic record
Abstract
Abstract Purpose: To investigate the effect of blue-light filtering intraocular lenses on drusen formation in postmortem eyes via histopathological analysis. Materials and Methods: One hundred and ninety-three postmortem pseudophakic eyes (100 with a clear intraocular lenses [cIOL] and 93 with a yellow, yellow intraocular lenses [yIOL]) were obtained from the Lions Gift of Sight eye bank. Sex, age-at-surgery, age-at-death, surgery-to-death time, and clinical history were collected. Eyes were sectioned on their coronal and sagittal axes, and formalin-fixed paraffin-embedded macular cross sections were obtained. Sections were stained with hematoxylin and eosin and scanned with the Zeiss Axio Scan. Z1 scanner. Drusen were classified by type, size or subtype, and quantity. Results: Large, soft drusen were present in 49% ( n = 95) of eyes, 9% ( n = 17) had cuticular drusen, 16% ( n = 30) had hard drusen, and 26% ( n = 51) had no drusen. There were significantly more cIOL eyes with large, soft drusen ( P < 0.001). There were significantly more yIOL eyes with no drusen ( P < 0.0001). No significant differences in the presence of hard or cuticular drusen were found. yIOL eyes had significantly higher mean age-at-surgery ( P < 0.001) and mean age-at-death ( P < 0.05), while cIOL eyes had a significantly higher mean surgery-to-death time ( P < 0.05). Finally, significantly more yIOL eyes had a history of smoking ( P < 0.01) and hypertension ( P < 0.05), while significantly more cIOL eyes had a history of glaucoma ( P < 0.05). Conclusions: Large, soft drusen were significantly less prevalent in yIOL eyes than in cIOL eyes and significantly more yIOL eyes had no drusen. These findings suggest that yIOLs may prevent the incidence and development of age-related macular degeneration after cataract surgery.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".