Near-infrared video: A technique for dynamic documentation of vitreous floaters
Bibliographic record
Abstract
Purpose The purpose of this study is to describe a technique using near-infrared (NIR) video for the diagnosis and documentation of symptomatic floaters. Methods Subjects with symptomatic floaters were identified through electronic case notes review, in which there was a primary diagnosis of floaters, secondary to PVD or syneresis. The presence of vitreous floaters was evaluated with both 30 °NIR or fundus autofluorescence images and short, dynamic 30°NIR videos, in which subject is asked to briefly look away and refixate on the target. Three retinal specialists assessed both unseen still images and videos to determine the presence or absence of vitreous floaters. Group descriptive statistics and inter/interobserver percentage agreement were calculated using SPSS. Results Ninety-three eyes from 51 subjects (30 males and 21 females, mean age (±SD) 54 ± 14.7 years and baseline visual acuity 0.13 ± 0.49) were analysed. An underling diagnosis of PVD was noted in 31 eyes and syneresis in 62 eyes. Floaters were observed in 43% of the still images versus 96% of videos. Interrater agreement was 0.75 for still images and 0.96 for videos. Intraobserver agreement was 0.84-0.96 for still images and 1.0 for videos. Conclusions Dynamic NIR video is an objective imaging test for the detection and recording of floaters in symptomatic patients, demonstrating both superior interobserver and intraobserver test-retest reliability to static fundal imaging. This technique helps visualize and assess symptomatic vitreous floaters, offering objective documentation of their presence or absence. It aids in pre-operative decisions, patient education, and post-operative comparisons.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".