The impact of a preloaded intraocular lens delivery system on operating room efficiency in routine cataract surgery
Bibliographic record
Abstract
PURPOSE: The aim of this study was to evaluate the operational impact of using preloaded intraocular lens (IOL) delivery systems compared with manually loaded IOL delivery processes during routine cataract surgeries. METHODS: Time and motion data, staff and surgery schedules, and cost accounting reports were collected across three sites located in the US, France, and Canada. Time and motion data were collected for manually loaded IOL processes and preloaded IOL delivery systems over four surgery days. Staff and surgery schedules and cost accounting reports were collected during the 2 months prior and after introduction of the preloaded IOL delivery system. RESULTS: The study included a total of 154 routine cataract surgeries across all three sites. Of these, 77 surgeries were performed using a preloaded IOL delivery system, and the remaining 77 surgeries were performed using a manual IOL delivery process. Across all three sites, use of the preloaded IOL delivery system significantly decreased mean total case time by 6.2%-12.0% (P<0.001 for data from Canada and the US and P<0.05 for data from France). Use of the preloaded delivery system also decreased surgeon lens time, surgeon delays, and eliminated lens touches during IOL preparation. CONCLUSION: Compared to a manual IOL delivery process, use of a preloaded IOL delivery system for cataract surgery reduced total case time, total surgeon lens time, surgeon delays, and eliminated IOL touches. The time savings provided by the preloaded IOL delivery system provide an opportunity for sites to improve routine cataract surgery throughput without impacting surgeon or staff capacity.
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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 | 0.002 | 0.015 |
| 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 | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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".