Bibliographic record
Abstract
The emphasis and location of pathology within veterinary curricula have undergone much change over recent years, and yet pathology can be viewed as a central ‘‘cog’’ in the whole curriculum. Traditionally this cog was located at the mid-point of curricula, forming the bridge between pre-clinical and clinical work. In this context, we can learn much from the human medical world—in particular, the change to more integrated curricula that occurred in the United Kingdom in wake of the ‘‘Tomorrows Doctors’’ documents, as described in this issue by Reid. The resulting concern from the ‘‘disciplines’’ about the consequences of potential loss of identity within these new integrated curriculum models was profound with a danger, as described elsewhere, that ‘‘while the medical education train accelerates away, pathologists are at risk of being left on the platform arguing the benefits of steam.’’ New and innovative approaches to learning and teaching in veterinary pathology using case-based approaches are described in this issue that embrace integration yet maintain the identity of the discipline. The utility of technology as it relates to ‘‘virtual microscopy’’ is also described in several articles—both for histopathology and for cytopathology—and there is good evidence to suggest real benefits to the quality of teaching and learning in this traditionally challenging area.
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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 | 0.006 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.002 | 0.004 |
| 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; both teacher heads agree on what is shown here.
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".