Infectious Disease Risk Factors of Corneal Graft Donors
Bibliographic record
Abstract
OBJECTIVE: To determine how donor health status affects the risk of infection after corneal transplant. METHODS: An adverse reaction surveillance registry was used to conduct a matched case-control study among transplanted donor corneas from January 1, 1994, to December 31, 2003. Cases comprised 162 reports of endophthalmitis after penetrating keratoplasty including 121 with microbial recovery, of which 59 had concordant donor and recipient microbial isolates. Two controls were matched to each case by surgery date. Conditional logistic regression estimated adjusted odds ratios with 95% confidence intervals according to the premortem status of decedent donors. RESULTS: Postkeratoplasty endophthalmitis was associated with recent hospitalization (odds ratio, 2.84; 95% confidence interval, 1.61-4.98) and fatal cancer (odds ratio, 2.46; 95% confidence interval, 1.53-3.97) among donors. Endophthalmitis appeared more likely with tissues transplanted longer than 5 days after donation (odds ratio, 1.55; 95% confidence interval, 1.02-2.35). The prevalence of concordant microbial isolates from donors and recipients was greater among fungal endophthalmitis than among bacterial endophthalmitis (P < .001). CONCLUSIONS: Corneal grafts with eye tissue obtained from donors dying in the hospital or with cancer may have an increased risk of postsurgical endophthalmitis, possibly due to donor-to-host microbial transmission. Together with donor screening and processing, improvements in microbiological control may reduce infection associated with corneal transplant.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".