Update on pediatric cataract surgery
Bibliographic record
Abstract
Cataracts in infants and children are comparatively rare, but they remain an important cause of potentially lifelong visual impairment, largely because of associated deprivation amblyopia. This is particularly seen in children missed by screening programs and thus presenting late for treatment. However, advances in diagnosis, surgical techniques and amblyopia management have improved the prognosis for most children seen with this condition. This comprehensive review focuses on all aspects of the care required to optimize outcomes. It covers modern genetic investigations, performed to precisely determine underlying cataract etiology, and discusses the use of outcome-based evidence to guide the timing of surgical intervention. The paper also outlines the options available to clinicians for post-operative refractive error correction and compares indications, risks and benefits for the use of contact lenses, spectacles and intraocular lenses (IOL). The challenge of choosing the most appropriate dioptric power of IOL to implant into a growing eye is discussed, as is consideration of types of IOLs that can be considered and the surgical techniques needed. Evidence-based approaches to the clinical management of amblyopia, glaucoma and visual axis opacification, the three most common complications seen following pediatric cataract surgery, are reviewed. Two specific conditions associated with pediatric cataract are discussed in detail--persistent fetal vasculature and ocular trauma. Strategies for assessment, management and surgical treatment of these conditions are reviewed.
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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.023 | 0.007 |
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".