Development of the <scp>McMaster</scp> Embalming Scale (<scp>MES</scp>) to assess embalming solutions for surgical skills training
Bibliographic record
Abstract
Human cadavers used for surgical training are embalmed using various methods to facilitate tissue storage and longevity while preserving the natural characteristics required to achieve high fidelity functional task alignment. However, there are no standardized means to evaluate the suitability of embalming solutions for this purpose. The McMaster Embalming Scale (MES) was developed to assess the extent to which embalming solutions allow tissues to achieve physical and functional correspondence to clinical contexts. The MES follows a five-point Likert scale format and evaluates the effect of embalming solutions on tissue utility in seven domains. This study aims to determine the reliability and validity of the MES by presenting it to users after performing surgical skills on tissues embalmed using various solutions. A pilot study of the MES was conducted using porcine material. Surgical residents of all levels and faculty were recruited via the Surgical Foundations program at McMaster University. Porcine tissue was unembalmed (fresh- frozen) or embalmed using one of seven solutions identified in the literature. Participants were blinded to the embalming method as they completed four surgical skills on the tissue. After each performance, participants evaluated their experience using the MES. Internal consistency was evaluated using Cronbach's alpha. Domain to total correlations and a g-study were also conducted. Formalin-fixed tissue achieved the lowest average scores, while fresh frozen tissue achieved the highest. Tissues preserved using Surgical Reality Fluid (Trinity Fluids, LLC, Harsens Island, MI) achieved the highest scores among embalmed tissues. The Cronbach's alpha scores varied between 0.85 and 0.92, indicating a random sample of new raters would offer similar ratings using the MES. All domains except odor were positively correlated. The g-study indicated that the MES is able to differentiate between embalming solutions, but an individual rater's preference for certain tissue qualities also contributes to the variance in scores captured. This study evaluated the psychometric characteristics of the MES. Future steps to this investigation include validating the MES on human cadavers.
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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 | 0.005 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".