Tissue recovery practices and bioburden: a systematic review
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
For successful transplantation, allografts should be free of microorganisms that may cause harm to the allograft recipient. Before or during recovery and subsequent processing, tissues can become contaminated. Effective tissue recovery methods, such as minimizing recovery times (<24 h after death) and the number of experienced personnel performing recovery, are examples of factors that can affect the rate of tissue contamination at recovery. Additional factors, such as minimizing the time after asystole to recovery and the total time it takes to perform recovery, the type of recovery site, the efficacy of the skin prep performed immediately prior to recovery of tissue, and certain technical recovery procedures may also result in control of the rate of contamination. Due to the heterogeneity of reported recovery practices and experiences, it cannot be concluded if the use of other barriers and/or hygienic precautions to avoid contamination have had an effect on bioburden detected after tissue recovery. Qualified studies are lacking which indicates a need exists for evidence-based data to support methods that reduce or control bioburden.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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 it