Bibliographic record
Abstract
Abstract Introduction The Liver Nodules Crash Course is a web-based module that facilitates self-learning of the pathology of liver nodules, including histology and diagnostic approach. It builds on previous work of similar modules, including our previously published GI Biopsy Crash Course and Liver Biopsy Crash Course. Methods The module features interactive elements to demonstrate the histological features of each liver nodule, as well as review questions integrated into the module. It is particularly unique in that it simulates looking at real histopathology slides and allows a medical trainee with minimal prior histopathology exposure to rapidly develop basic proficiency in liver nodules pathology—enough to function in liver pathology rounds. The module can be distributed to each learner or hosted on a learning management system. We expect that the performance of students completing the module will improve significantly, although this has not been formally evaluated yet. Results The overall response of learners who have completed this module has been overwhelmingly positive. Representative comments include: “I wish I had these a couple years ago… I wish there were more such modules on other topics,” and “These modules were really professional. The image quality is fantastic and the information is laid out very clearly. I learned some new things too.” Discussion The Liver Nodules Crash Course builds on our previous work in developing interactive self-learning modules for basic gastrointestinal and liver pathology. All of the modules in the series have been effective teaching tools for gastroenterology/hepatology fellows participating in pathology rounds, off-service residents rotating through pathology, and junior-level pathology residents.
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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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.204 | 0.076 |
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".