An Update on Viral Conjunctivitis Treatment Strategies: A Narrative Literature Review
Bibliographic record
Abstract
Viral conjunctivitis is a highly contagious ocular condition that significantly impacts patient quality of life and healthcare resources. Despite its self-limiting nature, the condition remains a significant public health concern due to its high transmissibility, prolonged symptoms, and potential complications such as subepithelial infiltrates (SEIs). This review aimed to synthesize and evaluate current management strategies for adenoviral conjunctivitis and provide an evidence-based treatment framework. A systematic literature search of PubMed and the Cochrane Library was conducted, identifying 25 eligible studies published between 2009 and 2024 that focused on clinical interventions including supportive care, antiseptics, corticosteroids, antivirals, and immune modulators. The findings indicate that while supportive therapy and hygiene measures remain central to care, antiseptic agents, specifically povidone-iodine, and topical steroids offer additional benefit in reducing symptom duration and complications. Combination therapies integrating antiseptics, corticosteroids, and immunomodulators show promise for more severe cases, especially those complicated by SEIs. This review proposes an evidence-based comprehensive, multimodal approach management algorithm while highlighting the need for future research in antiviral development and diagnostic innovation to avoid mistreatment and unnecessary antibiotic use.
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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.003 |
| Bibliometrics | 0.011 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".