Embalming cadaveric upper limbs after freezing and thawing: a novel technique for maximizing body donor usage through fresh frozen and formalin-fixed preservation
Bibliographic record
Abstract
Fresh frozen body donors are invaluable for surgical skills training sessions and medical research due to their realistic tissue quality. However, the potential for use as long-term teaching specimens is limited by soft-tissue deterioration following multiple freeze-thaw cycles. Embalming with the use of formalin achieves tissue fixation, thereby preventing tissue deterioration and enabling prolonged use of anatomical specimens. The purpose of this study was to determine whether fresh frozen upper limbs can be successfully embalmed for use as dissection and prosection resources in anatomical sciences education following one or more freeze-thaw cycles, thereby allowing for increased usage of an individual body donor. Four previously frozen left upper limbs were preserved using formalin fixation and were dissected 30 days following arterial embalming to determine whether adequate fixation could be achieved and whether the tissue quality could be maintained. The greatest number of freeze-thaw cycles evaluated in this study was six. To our knowledge, this is the first report in which specimens from fresh frozen human body donors have successfully been embalmed using formalin-fixation techniques following single or multiple freeze-thaw cycles. Following dissection of each upper limb, we conclude that formalin fixation after freezing and thawing is a viable preservation technique that can maintain a level of tissue quality suitable for educational dissection and prosection following use of the fresh frozen cadaver for surgical skills training sessions or medical research.
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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 | 0.000 | 0.000 |
| 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 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 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".