Bacterial Conjunctivitis in Childhood: Etiology, Clinical Manifestations, Diagnosis, and Management
Bibliographic record
Abstract
BACKGROUND: Bacterial conjunctivitis is a common reason for children to be seen in pediatric practices. A correct diagnosis is important so that appropriate treatment can be instituted. OBJECTIVE: To provide an update on the evaluation, diagnosis, and treatment of bacterial conjunctivitis in children. METHODS: A PubMed search was completed in Clinical Queries using the key term "bacterial conjunctivitis". Patents were searched using the key term "bacterial conjunctivitis" from www.freepatentsonline.com and www.google.com/patents. RESULTS: In the neonatal period, bacterial conjunctivitis is rare and the most common cause of organism is Staphylococcus aureus, followed by Chlamydia trachomatis. In infants and older children, bacterial conjunctivitis is most often caused by Haemophilus influenzae, Streptococcus pneumoniae, and Moraxella catarrhalis. Clinically, bacterial conjunctivitis is characterized by a purulent eye discharge, or sticky eyes on awakening, a foreign body sensation and conjunctival injection (pink eye). The diagnosis is made clinically. Cultures are unnecessary. Some authors suggest a watchful observation approach as most cases of bacterial conjunctivitis are self-limited. A Cochrane review suggests the use of antibiotic eye drops is associated with modestly improved rates of clinical and microbiological remission as compared to the use of placebo. Various investigators have also disclosed patents for the treatment of conjunctivitis. CONCLUSION: The present consensus supports the use of topical antibiotics for bacterial conjunctivitis. Topical antibiotics shorten the course of the disease, reduce discomfort, prevent person-to-person transmission and reduce the rate of reinfection.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".