Preoperative cataract grading by Scheimpflug imaging and effect on operative fluidics and phacoemulsification energy
Bibliographic record
Abstract
PURPOSE: To evaluate the power use, chamber stability, and surgical efficiency of a phacoemulsification system when cataracts were graded preoperatively using the Pentacam Nucleus Grading System (PNS) and adjustments were made in phaco parameters based on the cataract grade. SETTING: Royal Victoria Hospital, Barrie, Ontario, Canada. METHODS: Cataracts were graded using Scheimpflug imaging (Pentacam) in consecutive patients. In Group 1, surgery was performed with no change in parameters. In Group 2, adjustments were made preoperatively in fluidics and phaco power to reflect the cataract grade determined by Scheimpflug imaging. Parameters assessed in both groups included effective phaco time (EPT), balanced salt solution (BSS) use, and needle time to remove the cataract. RESULTS: There were 200 patients in each group. Emulsification and aspiration of higher and lower grades of cataract took statistically significantly less EPT and BSS in Group 2 (preoperative parameter adjustments) than in Group 1. The needle time for the higher grades of cataract was statistically significantly less in Group 2. For cataracts of a middle grade (2 to 3; 63% of cases), there was no statistically significant difference between standard phaco settings and adjusted settings. The cataract was effectively aspirated in both groups. CONCLUSION: Preoperatively adjusting phaco parameters based on cataract grade helped improve overall efficiency by reducing the amount of energy and fluid used in the eye and reducing overall phaco time.
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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 | 0.004 |
| 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 | 0.000 | 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".