Qualitative and quantitative comparison of Thiel and phenol‐based soft‐embalmed cadavers for surgery training
Bibliographic record
Abstract
INTRODUCTION: Surgical skills training has traditionally been limited to formalin embalming that does not provide a realistic model. The aim of this study was to qualitatively and quantitatively compare Thiel and phenol-based soft-embalming techniques: qualitatively in a surgical training setup, and quantitatively by comparing the mechanical and histomorphometric properties of skin specimens embalmed using each method. MATERIALS AND METHODS: Thirty-four participants were involved in surgical workshops comparing Thiel and phenol-based embalmed bodies. Participants were asked to evaluate the utility of the different models for surgical skills training. In parallel, tensile elasticity evaluation was performed on skin flaps from six fresh-frozen cadavers. Flaps were divided into three groups for each specimen: fresh-frozen, Thiel, and phenol-based embalmed and compared together at 1 month or 1 year after embalming. A histological investigation of the skin structural properties was performed for each embalming type using haematoxylin and eosin and Masson's trichrome. RESULTS: All participants rated the phenol-based specimens consistently better or equivalent to Thiel for the evaluated parameters. Quantitatively, there were statistically significant differences for the tensile elasticity between the embalming techniques (p < .05). There were no significant differences for the tensile elasticity between phenol-based embalmed skin and fresh state (p = .30), and no significant difference between embalming time was reported (p = .47). Histologically, the integrity of the skin was better preserved with the phenol-based technique. CONCLUSION: Phenol-based embalming provides as realistic or better of a model as Thiel embalming for surgical training skills and was generally preferred over Thiel model. The phenol-based embalming better preserved the integrity of the skin.
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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 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".