Structural integrity of intraocular lenses with eyelets in a model of transscleral fixation with the Gore-Tex suture
Bibliographic record
Abstract
PURPOSE: To compare fracture characteristics of intraocular lenses (IOLs) used in transscleral fixation with the Gore-Tex suture. SETTING: Department of Ophthalmology and Visual Sciences, Dalhousie University, Halifax, Nova Scotia, Canada. DESIGN: Experimental study. METHODS: A model was designed to compare the eyelet fracture characteristics of enVista MX60 IOL (model available before June 2018), the enVista MX60E IOL (current model), the Akreos AO60 IOL, and the CZ70BD IOL. Tension was applied through the Gore-Tex suture and measured with a digital force gauge. Two suture configurations (radial and nonradial) were tested using the MX60E IOL. RESULTS: A total of 25 trials were conducted. The mean eyelet fracture force was 1.666 newtons (N) for the MX60 IOL (range, 1.000-2.000), 1.000 N for the MX60E IOL (range, 1.000-1.000), 2.330 N for the AO60 IOL (range, 2.000-3.000), and 0.998 N for the CZ70BD IOL (range, 0.990-1.000). When compared with the MX60E IOL, a greater eyelet strength was observed with the MX60 (P = .024) and AO60 (P = .004) IOLs. Radial and nonradial suture configurations did not affect the MX60E eyelet fracture force. CONCLUSIONS: The enVista MX60E eyelet may be less resistant to Gore-Tex suture tear out compared with the MX60 and AO60 IOLs. Altering suture configuration did not affect enVista eyelet resistance to fracture. During off-label use in transscleral fixation, extra caution should be taken when handling IOLs and applying tension on sutures.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".