The development of a web‐based interactive tool to complement anatomy and radiology teaching and learning
Bibliographic record
Abstract
The ability to understand and interpret radiological images is a required competency in North American medical graduates. Prerequisite to this is a good knowledge of human anatomy. A radiology workshop, introduced as part of the Fundamentals of Body Design (FoBD) Year 1 UBC MD undergraduate program (MDUP), revealed our students’ need for a user‐friendly radiology resource to assist independent study of the material presented during the workshop. This study aimed to deliver such a resource to complement the teaching and learning of anatomy and radiology in the MDUP. Digital archives of normal X‐rays and CT scans, sourced from one of UBC's MDUP sites were accessed and compiled according to learning objectives of the FoBD radiology curriculum. Pixen ® graphics open source software was used to highlight key anatomical structures, based on FoBD objectives. Multiple colour images thus created for each radiological film were saved as distinct graphics layers, built into interactive html web pages using JavaScript ® and Cascading Style Sheets ® , and uploaded to www.clinicalanatomy.ca . UBC students currently accessing these images are able to study radiological anatomy interactively by repeatedly highlighting/unmarking structures of interest with the click of a mouse. Landmarks indistinct or vaguely apparent to the untrained eye are rendered more easily discernible, thus enriching the student's learning experience. Grant Funding Source : Source of funding: UBC Faculty of Medicine Undergraduate Program
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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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.042 | 0.013 |
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".