Usefulness of Prestorage Corneal Swab Culture in the Prevention of Contaminated Corneal Tissue in Corneal Transplantation
Bibliographic record
Abstract
PURPOSE: To assess the efficacy of the prestorage corneal swab (PCS) culture to screen for corneal graft contamination after storage in Optisol-GS. METHODS: A retrospective analysis of all PCS cultures was performed at the Eye Bank of Québec in Hôpital Maisonneuve-Rosemont from September 2013 to June 2016. Whole corneal culture was performed on rejected grafts because of a positive PCS, and a contamination rate was calculated. In addition, contamination rates of corneoscleral rims were compared between corneas tested with PCS and those of imported corneas which did not have PCS. RESULTS: Among the 1966 PCS cultures performed, 814 (41.4%) were positive for growth. Pathogenic bacteria were present in 144 (7.3%) corneas, including Staphylococcus aureus (n = 96, 11.8% of all positive cultures), Enterobacteriaceae (n = 14, 1.7%), and Pseudomonas aeruginosa (n = 6, 0.7%). After preservation in Optisol-GS, only 7 (6.9%) corneas remained contaminated (95% confidence interval 5.1-9.3). The sensitivity of the PCS culture was 87.5% (95% confidence interval 47.4-99.7). There was no significant difference in corneoscleral rim contamination between corneas tested with PCS (1/388; 0.2%) compared with imported, nonswabbed corneas (3/214; 1.4%) (P = 0.131). Therefore, the cost to recover the loss of tissue rejected because of false-positive PCS by purchasing corneal tissue was calculated to be $142,884 (CAD) per year. CONCLUSIONS: Despite the high sensitivity of PCS cultures, there was no significant reduction of infection after corneal transplantation using this technique. In consequence, 93% of the corneas possibly suitable for transplantation were rejected. This suggests that the PCS culture alone is a poor test for detecting clinically relevant corneal contamination.
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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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| 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".