Bacterial Keratitis in Toronto: A 16-Year Review of the Microorganisms Isolated and the Resistance Patterns Observed
Bibliographic record
Abstract
PURPOSE: To review the incidence, distribution, current trends, and resistance patterns of bacterial keratitis isolates in Toronto over the past 16 years. METHODS: Microbiology records of suspected bacterial keratitis that underwent a diagnostic corneal scraping and cultures from January 1, 2000, through December 31, 2015, were retrospectively reviewed. The distribution of the main isolated pathogens and in vitro laboratory minimum inhibitory concentration testing results were used to identify resistance patterns. RESULTS: A total of 2330 corneal scrapings were taken over 16 years. A pathogen was recovered in 1335 samples (57.3%), with bacterial keratitis accounting for 1189 of the positive cultures (86.0% of all isolates). The total number of gram-positive and gram-negative isolates was 963 and 324, respectively. Coagulase-negative Staphylococcus and Pseudomonas aeruginosa were the most common gram-positive and gram-negative bacteria isolates, respectively. A decreasing trend in the number of isolates in gram-positive bacteria (P = 0.01), specifically among Staphylococcus aureus (P < 0.0001) and Streptococcus species (P = 0.005), was identified. When analyzing the susceptibilities of gram-positive and gram-negative isolates, an increasing trend in antibiotic resistance was observed in erythromycin (P = 0.018), ceftazidime (P = 0.046), and piperacillin/tazobactam (P = 0.005). The susceptibility of tested gram-positive microorganisms to vancomycin was 99.6%. CONCLUSIONS: There has been a decreasing trend in the number of isolates in gram-positive microorganisms over the past 16 years. An increasing trend in resistance for various antibiotics against gram-negative and gram-positive isolates was identified. High susceptibility to vancomycin reinforced the empirical use of fortified tobramycin and vancomycin in the initial management of severe bacterial keratitis.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.005 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 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".