Visual Outcomes and Complications Over 5 Years Following Lensectomy for Childhood Traumatic Cataract
Bibliographic record
Abstract
Importance: The long-term outcomes of surgery for pediatric traumatic cataract are not well described; understanding these outcomes would inform parental expectations. Objective: To report visual acuity (VA) outcomes and cumulative incidence of ocular complications by 5 years following lensectomy for pediatric traumatic cataract. Design, Setting, and Participants: This prospective cohort study was conducted across 32 sites in the US and Canada among 75 children undergoing lensectomy from birth to less than 13 years of age for unilateral traumatic cataract. Of 75 children, 60 had primary intraocular lens (IOL) placement; 15 were left aphakic. Data were collected from August 2012 to February 2020. Data analysis was performed from October 2022 to May 2025. Exposure: Lensectomy. Main Outcomes and Measures: The primary outcomes were best-corrected VA 5 years after lensectomy; period prevalence of strabismus and glaucoma (including cases diagnosed before lensectomy); surgery for visual axis opacification (VAO); and cumulative incidence of ocular complications by 5 years. Annual data collection was from medical record reviews through 5 years following lensectomy. Results: Among 75 participants, 28 (37%) were female, and median (range) age at lensectomy was 7.4 years (0.1-13 years). Median (IQR) best-corrected VA at 5 years (available for 37 participants [49%]) was 20/63 (20/35-20/159) in pseudophakic eyes (n = 29; mean [SD] age at surgery, 7.2 [3.0] years) and 20/258 (20/56-<20/800) in aphakic eyes (n = 8; mean [SD] age at surgery, 5.5 [4.3] years). Age-normal VA was achieved by 6 pseudophakic eyes (21%; 95% CI, 10%-38%) and 1 aphakic eye (13%; 95% CI, 2%-47%). The 5-year period prevalence of glaucoma was 9% in pseudophakic eyes (95% CI, 1%-16%) and 9% in aphakic eyes (95% CI, 0%-24%). The 5-year cumulative incidence of surgery for VAO was 47% in pseudophakic eyes (95% CI, 31%-60%) and 13% in aphakic eyes (95% CI, 0%-28%). The risk was greater in eyes that did not undergo an anterior vitrectomy (84%; 95% CI, 55%-94%) vs eyes that did (15%; 95% CI, 2%-26%) (age-adjusted hazard ratio, 11.4; 95% CI, 4.6-33.1; P < .001). Conclusions and Relevance: In this cohort study, while only half of the study participants had a 5-year follow-up visit, achieving age-normal VA was uncommon among the cohort in whom a best-corrected VA was determined. The prevalence of glaucoma at 5 years was low among those with 5-year follow-up, while nearly half of the pseudophakic eyes needed surgery for VAO supporting the need to monitor these children.
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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 | 0.000 | 0.000 |
| 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) | 0.000 | 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".