Paediatric pseudophakia: analysis of intraocular lens power and myopic shift
Bibliographic record
Abstract
BACKGROUND: At the Alberta Children's Hospital, the authors have been performing paediatric cataract extraction with intraocular lens (IOL) implant for over 10 years. The authors examined the amount of myopic shift that occurs in various age groups and cataract types, in order to evaluate the success of predicting the appropriate power of IOL to implant. METHODS: This study is a retrospective review children undergoing small incision posterior chamber foldable IOL implantation between age 1 month and 18 years, from 1995 to 2005. 163 eyes of 126 patients underwent surgery. All patients were followed for a minimum of 6 months postoperatively. The children were divided into four groups at time of surgery: Group A: 1-24 months, Group B: 25-48 months, Group C: 49-84 months, Group D: 85 months-18 years. RESULTS: The mean target refraction for the groups were: Group A: +6.37 D, Group B: +4.66 D, Group C: +1.95 D, and Group D: +0.97 D. Children under 4 years experienced the most myopic shift and the largest mean rate of refractive change per year. Mean change Group A: -5.43 D, Group B: -4.16 D, Group C: -1.58 D, Group D: -0.71 D. Eighty-nine per cent of patients with unilateral cataracts had a postoperative refraction within 3.00 D of the fellow eye at last follow-up visit (mean=3.16 years). CONCLUSIONS: The rate of myopic shift is high in children under age 4 years at time of surgery, shifting as much as -12.00 D. The mean postoperative target refraction should probably be increased from previous literature recommendations. The patient's age at time of cataract surgery and the refractive power of fellow eye are all factors to consider when deciding what power IOL to surgically implant in a paediatric patient.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".