A quantitative, biomechanical analysis of radioulnar deviation between fresh frozen and soft embalmed human forearms
Bibliographic record
Abstract
Fresh frozen human cadaveric specimens are considered the gold standard for biomechanical research; however, the testing period of these specimens is limited because they decay rapidly, can be difficult to obtain and can carry infectious diseases. Therefore, various chemical fixation methods to deal with these issues have been developed. Traditional formalin fixation has been shown to significantly affect the biomechanical properties of the tissues; however, a relatively new method called soft embalming could provide an alternative to fresh frozen specimens in the field of biomechanical research. The purpose of this pilot study was to determine the effects soft embalming has on gross anatomical movements. The parameters for testing were radioulnar deviation range of motion with in plane and out of plane angulation measured using an incremented tendon loading protocol. Five fresh frozen human cadaveric forearms were obtained and injected with tantalum tracer beads and tested pre and post embalming using 3D X‐rays. The results showed that the soft embalmed specimens retained their flexibility and was slightly greater than the fresh frozen state. Although, at lighter loading trials, the range of motion in soft embalmed specimens was larger than the fresh frozen state. The results here may provide an argument for the inclusion of soft embalmed specimens in biomechanical testing.
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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) | 0.000 | 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 | 0.000 |
| Open science | 0.000 | 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".