Drug-induced corneal deposits: an up-to-date review
Bibliographic record
Abstract
This review assesses different clinical aspects of the various known drug-induced corneal deposits, based on the corneal layer involved (epithelium, stroma and/or endothelium), and based on the drug class. The most well-known condition caused by drug deposits is vortex keratopathy, or corneal verticillata, which is a whorl-like opacity in the corneal epithelium. Vortex keratopathy is commonly caused by certain cationic amphiphilic drugs such as amiodarone, antimalarials, suramin, tamoxifen, chlorpromazine and non-steroidal anti-inflammatory drugs. These deposits usually occur once a certain dose of the drug is reached. Most cases present with mild to moderate symptoms with minimal visual impairment. Most of these deposits resolve automatically, after months to years of drug cessation. Notably, other drug classes can cause deposits in all three layers of the cornea. Chlorpromazine, gold, rifabutin, indomethacin and tyrosine kinase inhibitors can cause stromal deposits, with reduced visual acuity when the anterior stroma is involved. Chlorpromazine and rifabutin can also cause deposits in the endothelial layer of the cornea. Regardless of the type of corneal deposit, local therapies such as topical lubricants or corticosteroids may help improve symptoms. Drug cessation or modification can also be helpful but should be weighed against the systemic risks of the underlying disease.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".