The LINDSAY Virtual Human Project: An immersive approach to anatomy and physiology
Bibliographic record
Abstract
The increasing number of digital anatomy teaching software packages challenges anatomy educators on how to best integrate these tools for teaching and learning. Realistically, there exists a complex interplay of design, implementation, politics, and learning needs in the development and integration of software for education, each of which may be further amplified by the somewhat siloed roles of programmers, faculty, and students. LINDSAY Presenter is newly designed software that permits faculty and students to model and manipulate three-dimensional anatomy presentations and images, while including embedded quizzes, links, and text-based content. A validated tool measuring impact across pedagogy, resources, interactivity, freedom, granularity, and factors outside the immediate learning event was used in conjunction with observation, field notes, and focus groups to critically examine the impact of attitudes and perceptions of all stakeholders in the early implementation of LINDSAY Presenter before and after a three-week trial period with the software. Results demonstrate that external, personal media usage, along with students' awareness of the need to apply anatomy to clinical professional situations drove expectations of LINDSAY Presenter. A focus on the software over learning, which can be expected during initial orientation, surprisingly remained after three weeks of use. The time-intensive investment required to create learning content is a detractor from user-generated content and may reflect the consumption nature of other forms of digital learning. Early excitement over new technologies needs to be tempered with clear understanding of what learning is afforded, and how these constructively support future application and integration into professional practice.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.018 | 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".