Histological characterization of soft‐embalmed porcine tendon and muscle (914.1)
Bibliographic record
Abstract
Embalmed material is used in many fields because of its benefits over fresh materials; however, typical formalin‐fixation results in materials that are vastly different from their original state. Soft‐embalming has received recognition as a viable alternative to fresh or formalin‐fixed materials in education and research, especially in orthopedics. Soft‐embalmed tissue has mainly been characterized by gross features, with minimal investigation into the microscopic features of different types of soft‐embalmed tissues. Deep flexor muscle and tendon were harvested from porcine forelimbs. Samples were soft‐embalmed using one of three different methods: phenol‐based, phenoxyethanol‐based, and Thiel embalming. The samples were visualized using Masson’s trichrome staining immediately following embalming and three weeks later. All types of soft‐embalming displayed typical tendon structural characteristics at both time points. The staining of the tendons ranged from almost entirely blue, as would be expected, to almost entirely red, which was unexpected, depending on the treatment. All soft‐embalmed muscle samples exhibited typical colouring, but there were structural differences. At both time points, muscle of phenol‐based and phenoxyethanol‐based embalming generally exhibited normal muscle structure, while Thiel‐embalmed muscle appeared to have suffered degradation. This histological analysis is a step forward in understanding the effects of different soft‐embalming methods on different tissues, which can aid in determining their efficacy. Grant Funding Source : This research was supported in part by NSERC and CIHR
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".