Bibliographic record
Abstract
Abstract Introduction We recognized a need at our institution for a resource to facilitate self-learning of basic liver histology and pathology. An interactive, web-based learning module was determined to be an ideal type of educational tool. The Liver Biopsy Crash Course is a self-learning resource for mastering basic liver histology and pathology. It is a useful aid for hepatology clinical fellows participating in liver pathology biopsy rounds and preparing for their exams. It is also useful for off-service/clinical residents rotating through pathology and for junior-level pathology residents. Methods The module includes an instructor's guide, the web-based crash course resource, and a quiz. Results We are beginning to implement the use of the Liver Biopsy Crash Course with hepatology clinical fellows participating in liver pathology biopsy rounds and with junior-level pathology residents going through their initial liver pathology rotations. After completion of the module, residents were surveyed and asked about the impact on their understanding of liver pathology and if they felt that the resource was of benefit to their training. Any additional feedback was also invited. The results from this pilot have been overwhelmingly positive. Samples of actual comments received are: “I wish I had these a couple years ago… I wish there were more such modules on other topics;” “… quite a good review of liver pathology and helped establish a good approach to separating the different entities. I found it very useful (and enjoyable) to work through;” and “… very helpful, especially for junior residents who (like myself) may not be very familiar or confident with looking at the liver.” Discussion We plan to further assess the effectiveness of the module in a second phase, which will involve assessing the learning impact on gastroenterology/hepatology fellows and off-service residents participating in liver biopsy rounds.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.216 | 0.063 |
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".