Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 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 | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.009 | 0.002 |
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 it