Diagnostic Yield of Corneal, Contact Lens, and Lens Fluid Cultures in Microbial Keratitis: A 12-Year Single-Center Study
Bibliographic record
Abstract
PURPOSE: To determine the yield of culturing contact lenses (CL) and contact lens fluid/cases (CLF) to isolate recognized pathogens in suspected microbial keratitis (MK). METHODS: Data from 4298 ocular samples of MK collected at the Royal Liverpool University Hospital between January 2012 and December 2023 were reviewed. The isolation rates and proportion of recognized pathogens were compared between corneal impression membranes (CIM) and scrapes (CS), CL, and CLF. Chi-square tests and mixed-effects logistic models were used to assess differences in distribution among all isolates and subgroups of samples. RESULTS: The overall isolation rate was 54.1%, with CL having the highest rate ([348/466], 74.7%), followed by CIM ([1822/2940], 62%), CS ([126/566], 22.3%), and CLF ([23/317], 7.3%). CLF, however, had the highest proportion of recognized pathogens ([22/28], 78.6%), followed by CL ([269/531], 50.7%), CS ([71/142], 50%), and CIM ([728/2218], 32.8%). CIM and CS predominantly detected Gram-positive bacteria, whereas CLF had the highest rates of Acanthamoeba ([13/28], 46.4%) and fungal isolates ([4/28], 14.3%). CL and/or CLF cultures led to altered treatment in 10.5% of cases. CONCLUSIONS: In suspected MK, culturing the CLF and CL can increase the probability of detecting a recognized pathogen that in turn may help guide treatment.
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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 | 0.002 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".