Disinfection of human musculoskeletal allografts in tissue banking: a systematic review
Bibliographic record
Abstract
Musculoskeletal allografts are typically disinfected using antibiotics, irradiation or chemical methods but protocols vary significantly between tissue banks. It is likely that different disinfection protocols will not have the same level of microorganism kill; they may also have varying effects on the structural integrity of the tissue, which could lead to significant differences in terms of clinical outcome in recipients. Ideally, a disinfection protocol should achieve the greatest bioburden reduction with the lowest possible impact on tissue integrity. A systematic review of three databases found 68 laboratory and clinical studies that analyzed the microbial bioburden or contamination rates of musculoskeletal allografts. The use of peracetic acid-ethanol or ionizing radiation was found to be most effective for disinfection of tissues. The use of irradiation is the most frequently published method for the terminal sterilization of musculoskeletal allografts; it is widely used and its efficacy is well documented in the literature. However, effective disinfection results were still observed using the BioCleanse™ Tissue Sterilization process, pulsatile lavage with antibiotics, ethylene oxide, and chlorhexidine. The variety of effective methods to reduce contamination rate or bioburden, in conjunction with limited high quality evidence provides little support for the recommendation of a single bioburden reduction method.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.004 |
| Bibliometrics | 0.007 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".