A laboratory evaluation of nozzle tip damage in four generations of intraocular lens injector systems using a self-developed damage scale
Bibliographic record
Abstract
During intraocular lens (IOL) implantation it is not uncommon for the injector's nozzle-tip to get damaged. However, the damage has not been systematically described or evaluated using an objective scale. In this study we developed our own system-the Heidelberg Score for IOL Injector Damage ("HeiScore"), which was used to grade 60 injectors from four generations of injector models (Monarch III D, AcrySert C, UltraSert, AutonoMe) made by the same manufacturer. (Alcon Laboratories Inc.) HeiScore has six grades of nozzle-tip damage: no damage (which was graded 0); slight scratches (1), deep scratches (2), extensions (3), cracks (4) and bursts (graded number 5). The score for each injector model was the sum of all grades (total number), and we could compare the four injector models. The injectors showed varying damage profiles, from "no damage" to "crack". A tendency of a lower damage score in the newer generations of IOL injectors was noted. However, a statistically significant difference was observed only between Monarch III D and AutonoMe. The "Heidelberg Score for IOL Injector Damage" could efficiently and effectively evaluate the damage to IOL injector systems, which might help manufacturers optimize the positioning of the IOL in the injector during pre-loading.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".