Innovative, Simple Models for Teaching Neuroanatomy Using the Elnady Technique
Bibliographic record
Abstract
Plastination is a valuable tool for the teaching of neuroanatomy. However, the high cost of the process and the complexity of sheet plastination for brain slices remains a challenge. This article describes an innovative, simple, and inexpensive method, called the Elnady Technique, to develop brain slices of various domestic animals. The slices are either enveloped in lamination sheets using an electric iron, or enveloped in transparent plastic using an impulse sealer. This fast, effortless process results in realistic, durable, odorless, soft, flexible slices. The models provide accurate three-dimensional (3D) reference guides for demonstration of neuroanatomical structures that show soft tissue contrast between the gray and white matter. This makes them invaluable for interpretation of clinical imaging modalities, such as computed tomography (CT) and magnetic resonance imaging (MRI). These ethically sourced models can provide a replacement for the killing of animals for practical classes.
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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 | 0.000 | 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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".