Bibliographic record
Abstract
Abstract Introduction We recognized a need at our institution for a resource to facilitate self-learning of basic gastrointestinal (GI) histology and pathology. In particular, we sought to produce an aid for GI clinical fellows participating in GI pathology biopsy rounds and preparing for their exams. We also wanted to develop a self-learning tool for other off-service/clinical residents rotating through pathology and junior-level pathology residents (within their first 2 years of residency). An interactive, web-based learning module was determined to be an ideal type of educational tool. Methods The GI Biopsy Crash Course consists of two file folders, GI Path Module and GI Crash Quiz, contained in one module, as well as an Instructor's Guide. Students work through GI Path Module, which is the teaching module, first; they then take the GI Crash Quiz after completing the teaching module. The quiz consists of 10 questions. Approximately 1-2 hours are required to work through GI Path Module and GI Crash Quiz, with most of that time spent in the teaching module. Results We have implemented the GI Biopsy Crash Course with GI clinical fellows participating in GI pathology biopsy rounds and with junior-level pathology residents going through their initial GI pathology rotations. The performance of students completing the module, although not formally evaluated, has improved noticeably. Feedback from students who have used the module has been very positive. Discussion The level of material in the GI Biopsy Crash Course is mainly introductory. We hope to create additional, higher level modules that deal with more advanced topics in GI pathology in the future.
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.
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 | 0.002 |
| 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) | 0.169 | 0.064 |
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".