Piggyback intraocular lens implantation to correct myopic pseudophakic refractive error after penetrating keratoplasty
Bibliographic record
Abstract
PURPOSE: To determine the safety and efficacy of implanting a second intraocular lens (IOL) to correct myopic pseudophakic refractive error after penetrating keratoplasty (PKP). SETTING: Department of Ophthalmology, Toronto Western Hospital, Toronto, Ontario, Canada. METHODS: In this retrospective case series, 6 eyes of 6 post-PKP pseudophakic patients had a second piggyback IOL implantation to correct a residual myopic refractive error. The uncorrected visual acuity (UCVA) and the best corrected visual acuity (BCVA) were measured at regular intervals during a 7-month follow-up. Efficacy was determined by the achieved refractive correction and Snellen UCVA measurements. Safety was measured by loss of BCVA and complications (intraoperative and postoperative). RESULTS: The UCVA improved in all cases. Five patients achieved a BCVA of 20/40 or better postoperatively. Before surgery, the mean spherical equivalent (SE) was -8.08 diopters (D) (range -6.13 to -12.00 D). After surgery, the mean SE was -0.94 D (range -2.38 to +0.25 D). Four patients were within +/-1.50 D of emmetropia. There were no intraoperative or postoperative complications. CONCLUSION: Implanting a piggyback IOL was a safe and effective means of correcting myopic pseudophakic refractive error post PKP.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".