Pre‐clinical evaluation of bone allograft toughened with a novel sterilization method: An in vivo rabbit study
Bibliographic record
Abstract
Bone allografts often undergo γ-irradiation sterilization to decrease infection risk. However this consequently degrades bone collagen and makes the allograft brittle. Our laboratory has previously found that pre-treatment with ribose ex vivo protects the bone. However, it remains unclear whether or not ribose-treated γ-irradiated allografts are able to unite and remodel in vivo. Using New Zealand White rabbits (NZWr), we aimed to evaluate if ribose-treated allografts can unite with host bone (compared to untreated (fresh-frozen) and conventionally-irradiated allografts). A critically-sized defect was created in the radii of NZWr and reconstructed with allografts fixed with an intramedullary Kirschner wire. Healing and union were assessed at 2, 6, and 12 weeks post operation, with radiographs, µCT, static and dynamic histomorphometry, backscatter electron microscopy, and torsion testing. Intramedullary fixation achieved stable reconstructions and bony union in all groups and no differences were found in the radiographic and biomechanical parameters tested. Interestingly, γ-irradiated allografts had significantly less bone volume due to evident resorption of the grafts. In contrast, ribose pre-treatment protected γ-irradiated allografts from this bone loss, with results similar to the fresh frozen controls. In conclusion, ribose-pretreated γ-irradiated allografts were able to unite in vivo. In addition to achieving bony union with host bone, ribose pre-treatment may protect against allograft resorption. © 2019 Orthopaedic Research Society. Published by Wiley Periodicals, Inc. J Orthop Res.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".