Intraoperative performance and postoperative outcome comparison of longitudinal, torsional, and transversal phacoemulsification machines
Bibliographic record
Abstract
PURPOSE: To compare the intraoperative performance and postoperative outcomes of 3 phacoemulsification machines that use different modes. SETTING: Kensington Eye Institute, Toronto, Ontario, Canada. DESIGN: Comparative case series. METHODS: This chart and video review comprised consecutive eligible patients who had phacoemulsification by the same surgeon using a Whitestar Signature Ellips-FX (transversal), Infiniti-Ozil-IP (torsional), or Stellaris (longitudinal) machine. RESULTS: The review included 98 patients. Baseline characteristics in the groups were similar; the mean nuclear sclerosis grade was 2.0 ± 0.8. There were no significant intraoperative complications. The torsional machine averaged less phacoemulsification needle time (83 ± 33 seconds) than the transversal (99 ± 40 seconds; P=.21) or longitudinal (110 ± 45 seconds; P=.02) machines; the difference was accentuated in cases with high-grade nuclear sclerosis. The torsional machine had less chatter and better followability than the transversal or longitudinal machines (P<.001). The torsional and longitudinal machines had better anterior chamber stability than the transversal machine (P<.001). Postoperatively, the torsional machine yielded less central corneal edema than the transversal (P<.001) and longitudinal (P=.04) machines, corresponding to a smaller increase in mean corneal thickness (torsional 5%, transversal 10%, longitudinal 12%; P=.04). Also, the torsional machine had better 1-day postoperative visual acuities (P<.001). CONCLUSIONS: All 3 phacoemulsification machines were effective with no significant intraoperative complications. The torsional machine outperformed the transversal and longitudinal machines, with a lower mean needle time, less chatter, and improved followability. This corresponded to less corneal edema 1 day postoperatively and better visual acuity.
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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 | 0.009 |
| 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 | 0.001 |
| 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".