A 14-Year Audit and Analysis of Human Skin Allograft Discards
Bibliographic record
Abstract
The objective of this study was to review the incidence of skin allograft discard and bacterial contamination due to donor bioburden at the Ontario Professional Firefighters Skin Bank over a 14-year period. We sought to determine whether modifications to our prerecovery donor preparation process have been effective in reducing skin bioburden and identify other potential risk factors of allograft contamination. A retrospective review of all skin donors (n = 259) processed from 2002 to 2015 was performed. Multivariate logistic regression was used to determine whether donor-related factors and procurement-related factors were significantly associated with microbial contamination predisinfection and discard secondary to contamination. Eighty-one donor recoveries were discarded (81/259; 31%) or 694 grafts (694/2636; 26%), with bacterial contamination being the most common reason for discard (29/81; 36%) followed by positive viral serology (21/81; 26%) primarily for hepatitis B core antibodies. Bacterial contamination predisinfection was detected in 46% of donors (49% of grafts). Analysis of risk factors showed that only donor preparation using a 70% alcohol spray significantly reduced odds of both bacterial contamination predisinfection (P < .0001) and discard secondary to bacterial contamination (P = .0233). Our results suggest that selective screening of donors to reduce risk of microbial contamination is unlikely to alter the rate of allograft contamination. However, use of a 70% alcohol spray during donor preparation may minimize bacterial contamination and subsequent bacterial-related discards. Given that detailed guidelines for donor preparation do not exist, use of an alcohol spray may be of value for tissue banks experiencing allograft loss due to bacterial 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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".