Effect of Cataract Extraction on the Visual Fields of Patients With Glaucoma
Bibliographic record
Abstract
OBJECTIVE: To investigate the effect of cataract extraction on the visual fields of patients with open-angle glaucoma. METHODS: Patients in a prospective cohort study in a tertiary center underwent standard automated perimetry every 6 months. We compared the mean results of the 2 examinations immediately before and 2 examinations immediately after phacoemulsification cataract extraction and intraocular lens implant (effect analysis) and the mean results of the first 2 and last 2 examinations from 4 consecutive examinations obtained more than 1 year after the cataract surgery (control analysis). RESULTS: Our sample contained 34 eyes of 26 patients (mean +/- SD age, 69.2 +/- 10.8 years). While the mean logMAR best-corrected visual acuity improved significantly by approximately 2 Snellen lines after surgery (P < .001), the average change in mean deviation in both the effect and control analyses was less than 0.1 dB and not statistically significant (P = .85). There was a strong correlation between change in foveal sensitivity and change in mean deviation in the effect analysis but not in the control analysis (r = 0.76 [P < .001] and r = 0.30 [P = .08], respectively). There was no relationship between change in visual acuity or initial mean deviation and change in mean deviation in either analysis. Change in pointwise total deviation was not systematically related to the respective baseline value in either analysis; however, the variance of the distribution of change in total deviation was significantly higher in the effect analysis (P < .001). CONCLUSION: While there was an improvement in best-corrected visual acuity after cataract surgery, the changes in the visual field as a group were negligible.
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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| 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) | 0.001 | 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".