Development of a Hands-On and Virtual Simulation Training Module To Teach Microtomy
Bibliographic record
Abstract
BACKGROUND: Microtomy is a risky procedure that medical laboratory technologists (MLTs) use to cut tissue samples for microscopic examination. Due to the safety concerns and the potential to destroy tissue samples, it is critical for learners to perform the procedure correctly. To allow for safe and controlled learning, this procedure should be acquired in a safe and controlled simulated setting before being attempted on human tissues. The overarching purpose of this work is the development of a virtual training module for undergraduate students to learn from. However, because of the heterogeneity in the steps required to successfully complete the procedure from the MLTs as well as in the literature, the aim of this study was to reach a consensus from a panel of experts about identifying the steps of the procedure using the think-aloud and modified-Delphi methods. METHODS: First, we conducted a think-aloud protocol with a single MLT expert trained in microtomy to generate the list of steps of the microtomy procedure objectively. In order to remove any idiosyncratic steps, next, we asked eight experts that were trained in histology to rate the criticalness of each step using a (1-5) Likert scale and provide evaluative feedback. RESULTS: The think-aloud protocol generated 10 steps for the microtomy procedure. During the subsequent two rounds of the Delphi exercise, the experts agreed to modify one step of the 10 steps. CONCLUSIONS: Through this work, the 10 steps of the microtomy procedure have been validated by experts in the field. Following that, a virtual simulation training module was built to instruct learners on the microtomy procedure. The virtual simulation training module may be used for further research in microtomy.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".