The teaching of human Anatomy in Spain at the beginning of the twenty-first century
Bibliographic record
Abstract
This work, which merely aims to reflect the authors' opinions, begins by pointing out a series of problems involved in the teaching of Human Anatomy in Spanish Universities over the past quarter century. Emphasis is placed on both the causes that can be attributed to anatomists themselves and on those deriving from University legislation, especially after the University Reform Law. We also refer to the small body of literature published by Spanish anatomists with respect to the teaching of Anatomy, cadaver dissection or the use of new technologies in such teaching. We then offer an overview of the current circumstances and future possibilities not only of teaching and methodology but also as regards instructors and departments. We offer a reference to the Spanish Anatomical Society and we end by proposing a series of guidelines for the immediate future with a view to optimising the teaching of Human Anatomy and Embryology and the professionalisation of future anatomists.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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".