Can virtual dissection be effectively performed remotely? Pilot study from a second‐year neuroanatomy laboratory at a large distributed medical school
Bibliographic record
Abstract
Background Virtual dissection is an emerging area in undergraduate medical anatomy teaching as it incorporates clinical radiology into students' dissection experience. Virtual dissection is performed on near life‐size anatomy visualization tables (AVTs), which are very similar to hospital radiology workstations. Patient CT scans are loaded into these tables and through powerful software interactions students work together to manipulate the data and perform their dissection. Recent technology developments have allowed for some of the virtual dissection functionality to be accessed remotely on hand‐held devices (e.g. tablets). While this makes the virtual dissection experience feasible in a distributed program, it is unclear whether this “lighter” version of the software provides students with the same learning opportunities as virtual dissection performed on an AVT. Methods During a second‐year cadaveric neuroanatomy laboratory, 288 medical students were invited to use an online application to access content from an AVT remotely. Students accessed and examined three clinical radiology cases on their device at both the main teaching campus as well as at the distributed campuses. Following the laboratory session, students completed an anonymous online survey to assess their experience. All students had performed virtual dissection on an AVT during their first year of medical school allowing them to compare their experiences. Results The survey response rate was 7.9% across four separate campuses. 52.2% of students were located on the main campus and 47.8% were from one of the distributed campus. Most students (74.0%) “agreed” or “strongly agreed” that virtual dissection enhanced their understanding of the cadaveric content presented in the laboratory and their understanding of radiology anatomy. In addition, most students 74.0%) “agreed” or “strongly agreed” that virtual dissection enhanced their awareness of the clinical applications of the anatomy. Students reported that they would have liked to have more ability to virtually dissect the cases and more time to study them. Conclusions Virtual dissection is a valuable addition to a second‐year medical undergraduate neuroanatomy cadaveric laboratory. However, students reported that performing the dissection on their tablets was not as effective as performing it on the AVT. This abstract is from the Experimental Biology 2018 Meeting. There is no full text article associated with this abstract published in The FASEB Journal .
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".