Development of a virtual 3D renal corpuscle for educational environments
Bibliographic record
Abstract
Histology is a challenging educational discipline. It requires studying 2‐dimensional (2D) microscopic sections of tissue and inferring three‐dimensional (3D) organization. Gross anatomical education has benefitted from the burgeoning use of 3D models and images, however, 3D digital models of histological structures have not been developed for educational purposes. To overcome these limitations we have developed a 3D histological model of a renal corpuscle (RC) based on serial histological sections for e‐learning environments. Sprague Dawley rat kidneys were fixed, dehydrated, and embedded in epoxy resin (Embed‐812). Ribbons of serial semi‐thin sections (1μm thick) were obtained using a diamond knife and ultramicrotome. The sections containing the RC (n=179) were digitized and the images were aligned with the 3D visualization software, Amira 5.2, for 3D model development. Key structures of the RC and surrounding tissue were digitally reconstructed via manual segmentation. The resulting digital model allows for the generation of RC images in any plane with resolutions comparable to that of the original images. Users may view digitally generated histological sections in the context of the 3D renal corpuscle, enabling user interactivity and visualization in a variety of 2D or 3D orientations. Future plans for the model include developing an e‐learning module and subjecting it to usability tests. Grant Funding Source : Instructional Innovation and Development Fund (IIDF)
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| 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) | 0.006 | 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".