Bacterial Keratitis: Clinical Features, Causative Organisms, and Outcome During a 13-year Study Period
Bibliographic record
Abstract
PURPOSE: This study aimed to review clinical features, causative organisms, complications, and outcome of bacterial keratitis cases at a tertiary eye hospital. METHODS: A retrospective study was conducted on clinically diagnosed bacterial keratitis cases from 2007 to 2019. Poor outcome was flagged if any of the following was identified: final visual acuity (VA) worse than 20/200, decrease in VA (1 line or worse compared with presenting VA), corneal perforation, endophthalmitis, failed graft, or cases requiring enucleation or evisceration. RESULTS: The study included 263 cases of bacterial keratitis with 169 cases (64.3%) of culture-positive bacterial keratitis. Gram-positive bacteria were found to be the causative organism in 106 cases (62.8%). The most common types were coagulase-negative staphylococci (23.1%) and Pseudomonas (23.1%). Culture-positive bacterial keratitis was associated with the development of anterior chamber reaction (≥1+) on multivariate analysis [adjusted odds ratio (OR): 3.03, confidence interval (CI): 1.23-7.45, P = 0.016]. The complications that occurred in the current cohort included visually significant scar (64.7%), perforation (10.8%), cataract (8.8%), nonhealing epithelial defects (8.0%), corneal neovascularization (4.9%), endophthalmitis (4.6%), and hypotony (1.5%). On multivariate analysis, diabetes mellitus (adjusted OR: 3.51, CI: 1.59-7.76, P = 0.002), poor presenting best-corrected VA (adjusted OR: 3.95, CI 1.96-7.96, P < 0.001), and positive cultures (adjusted OR: 2.36, CI: 1.11-5.00, P = 0.025) were associated with poor outcome. CONCLUSIONS: Culture-negative keratitis had less severe infection and better outcomes when compared to culture-positive bacterial keratitis. Factors associated with poor outcome included diabetes, poor presenting VA, and positive cultures.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".