<p>Secondary Posterior Chamber Intraocular Lens Fixation Using Flanged Prolene Sutures as an Alternative to an Anterior Chamber Intraocular Lens Implantation</p>
Bibliographic record
Abstract
PURPOSE: To describe and evaluate the clinical outcomes of the Flanged Prolene Suture intraocular lens fixation (PIF) technique and compare it to anterior chamber IOL (ACIOL) implantation. DESIGN: A retrospective comparative review. METHODS: A retrospective comparative review of consecutive patients undergoing secondary IOL implantation was performed. A comparison between patients that had ACIOL and PIF technique was conducted. The main outcome measures were changes in best-corrected visual acuity (VA), IOL position and complications. RESULTS: In the study period, fourteen eyes had ACIOL implantation and ten eyes had PIF surgery. VA acuity for both groups combined improved from 1.27 ± 0.65 logMAR preoperatively to 0.84 ± 0.65 logMAR (P<0.0001). Seventeen patients had VA measurements in the year before the IOL dislocation. In those seventeen patients, VA changed from a baseline of 0.90 ± 0.68 to 0.97 ± 0.61 logMAR in the PIF group (p=0.334) and from 0.54 ± 0.27 to 0.85 ± 0.65 logMAR in the ACIOL group (p=0.145). No intraoperative or early postoperative complications were documented in either group. Two (20%) patients in the PIF group developed CME and one patient developed corneal edema. In the ACIOL group, one patient developed significant CME and two patients developed visual significant corneal edema. CONCLUSION: The PIF technique seems to offer a simple, fast and safe way to fixate an IOL posteriorly. In our experience, the learning curve of the technique is short with a low complication rate and good visual outcomes.
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 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 | 0.002 |
| 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 | 0.000 | 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".