The impact of cataract on the quantitative, non‐invasive assessment of retinal blood Flow
Bibliographic record
Abstract
PURPOSE: The aim of the study was to determine the impact of cataract on the quantitative, non-invasive assessment of retinal blood flow assessed by bidirectional laser Doppler flowmetry and simultaneous vessel densitometry. METHODOLOGY: Ten patients scheduled for extracapsular cataract extraction using phacoemulsification and intraocular lens implantation between the ages of 61 and 84 (mean age 73 years, SD ± 8) were prospectively recruited. Two visits were required to complete the study; one visit prior to extracapsular cataract extraction and one at least 6 weeks after the surgery to allow for sufficient postoperative recovery. The severity of cataract was documented using the Lens Opacity Classification System (LOCS, III) at the first visit. Retinal arteriolar hemodynamics were measured at both visits using the high-intensity setting of the Canon Laser Blood Flowmeter. RESULTS: All eyes showed no clinical signs of postoperative intraocular inflammation. The quantitative assessment of retinal arteriolar diameter and blood flow were reduced following extracapsular cataract extraction (Wilcoxon signed-rank test, p = 0.022 and p=0.028, respectively); however, centreline blood velocity was not significantly changed (Wilcoxon signed-rank test, p=0.074). Intraocular pressure was unchanged pre- and postcataract extraction. CONCLUSIONS: Retinal vessel densitometry assessment in the presence of cataract results in the erroneous elevation of the diameter measurement and thereby the calculation of blood flow. The bidirectional Doppler assessment of blood velocity appears to be more robust to light scatter induced by cataract. Care needs to be exercised in the interpretation of studies of retinal vessel diameter or blood flow that utilize similar densitometry techniques.
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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 | 0.005 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".