Assessment of retinal arteriolar hemodynamics in patients pre‐ and post‐cataract extraction
Bibliographic record
Abstract
Abstract Purpose We have previously demonstrated that artificial light scatter results in the erroneous elevation of retinal vessel diameter and blood flow using densitometry based techniques. The aim of this study was to determine the impact of cataract on the quantitative, non‐invasive assessment of retinal arteriolar blood flow. Methods Of the original 30 recruited patients who were scheduled for extra‐capsular cataract extraction using phacoemulsification and intraocular lens implantation, ten patients between the ages of 61 and 84 (mean 73 ± 8) successfully completed the study protocol. Two visits were required to complete the study, one prior to the surgery and one at least six weeks after the surgery. Cataract status was documented using the Lens Opacity Classification System (LOCS, III) on the first visit. Retinal arteriolar hemodynamics were measured using the high intensity laser setting of the Canon Laser Blood Flowmeter (CLBF) on each visit. Results Group mean retinal arteriolar diameter and blood flow were significantly lower following extracapsular cataract extraction (Wilcoxon signed‐rank test, p=0.022 and p=0.028 respectively); however, centreline blood velocity was unchanged (Wilcoxon signed‐rank test, p=0.074). The primary reason for failure to complete the study protocol was due to poor retinal image quality impairing CLBF measurement. Conclusion Densitometry assessment of vessel diameter is extraneously impacted by the presence of cataract. Care needs to be exercised in the interpretation of studies of retinal vessel diameter and blood flow that utilize 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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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".