Preliminary Multi‐Site Usage For The Online ‘Medical Imaging Solution For Teaching And Research’ (MISTR)
Bibliographic record
Abstract
INTRODUCTION Clinical imaging is one of the most common ways medical professionals experience anatomy during their careers it is, however, only fractionally covered in the early years of medical school. This is related, in no small part, to the wholesale reduction in anatomical teaching time experienced by most programs. Additionally, there are concerns with lack of educator experience, and lack of representative images. Ideally, students need to study clinical imaging in a self‐directed manner on their own time. Anatomical teaching at UMass medical school is in the unique position of being under the purview of the Department of Radiology, allowing unprecedented access to trained educators and clinical imaging. METHODS Utilizing the previously described (Carter et al ., 2015) ‘Medical Imaging Solution for Teaching and Research’ (MISTR), an online image education resource, created at the University of Saskatchewan, a multi‐site collaborative program was designed. This education tool included access to the MISTR‐Repository, a library of clinical image cases of both normal and variant anatomy, online case‐based education modules, and vitally, O.D.I.N. a unique online dicom image navigator that preserves image resolution and facilitates easy image access. DISCUSSION The key to MISTR's success is the modular nature of the program. Multiple instructors can utilize individual clinical images in multiple ways. New cases can be built utilizing the same images without disturbing other cases. Purpose built plugins for ‘Blackboard’ the learning management system used by both sites, allows a portal for all instructors to access all cases and to quickly allow instructors to associate images or cases with their coursework. The success of the program will continue to be tested with the release of the beta version in early 2016. SIGNIFICANCE Integrating clinical imaging earlier into the medical curriculum and closely associating it with gross anatomy allows students to better assimilate their understanding of anatomical structures as viewed in dissection and text books with that seen though different imaging modalities, long term we suggest this will enable easier comprehension of advanced clinical imaging and patient outcomes. Additionally, the creation of an online viewer allows students to work in a simulated clinical imaging environment at their own pace, in their own time.
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How this classification was reachedexpand
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 | 0.003 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".