Effect of microinterventional endocapsular nucleus disassembly using centripetal loop fragmentation on refractive outcomes after cataract surgery
Bibliographic record
Abstract
PURPOSE: To evaluate the refractive impact of centripetal fragmentation using the miLOOP system for nucleus disassembly, which minimizes lens zonulocapsular instability associated with endocapsular lens manipulation. SETTING: Private practice, Batesville, Indiana, USA. DESIGN: Retrospective comparative consecutive series. METHODS: Refractive outcomes were compared for consecutive patients who underwent cataract surgery and intraocular lens implantation before and after the introduction of a microinterventional endocapsular nucleus disassembly technique using the miLOOP system. Eyes with a history of previous surgery or ocular comorbidities were excluded. The primary outcome was the median absolute error (MedAE) from the preoperative predicted refraction. Secondary outcomes included corrected (CDVA) and uncorrected distance visual acuity (UDVA) and the proportion of eyes within predicted diopter (D) ranges. RESULTS: A total of 118 eyes of 79 patients were analyzed, with 69 eyes undergoing conventional nuclear disassembly and 49 eyes receiving the microinterventional technique. The MedAE for eyes using conventional nucleus disassembly vs the microinterventional technique was 0.191 D vs 0.107 D, respectively (P = .002). For CDVA and UDVA, the microinterventional approach resulted in a trend toward a higher proportion of eyes achieving acuities better than 20/30, 20/25, and 20/20 compared with conventional techniques. The microinterventional approach showed a trend toward more eyes achieving less than ±0.25 D and ±0.50 D of prediction error from the predicted diopter range. CONCLUSIONS: Microinterventional nuclear disassembly might improve refractive outcomes by reducing refractive prediction error.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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".