OPTIMAX 2018 - a focus on education in radiology
Bibliographic record
Abstract
This year, OPTIMAX was warmly welcomed by University College Dublin. For the sixth time students and teachers from Europe, South Africa, South America and Canada have come together enthusiastically to do research in the Radiography domain. As in previous years, there were several research groups consisting of PhD-, MSc- and BSc students and tutors from the OPTIMAX partner Universities or on invitation by partner Universities. OPTIMAX 2018 was partly funded by the partner Universities and partly by the participants. This year, five research projects were performed with a focus on education on dose- and image quality optimization. The research projects were: CT Simulation as an Active learning tool Redesigning a Radiography Practical Active Learning Space Does Radiographer Training Across Europe Alter Image Viewing Patterns and Decisions? An Investigation into the Use of Lead Shielding Protection in Abdominal Radiography Inter-user Variability in DXA Scanning and Analysis The summer school was concluded with a poster session and a conference, where the research teams presented their results. All five abstracts were submitted to the European congress of Radiology (ECR) and, when accepted, will be presented by the students as posters, or oral presentations. This book comprises of two sections, the first section contains several chapters about new educational applications for Radiology Education. The second section contains the research papers of the five research projects. Steering committee OPTIMAX 2018 Hogg P, School of Health Sciences, University of Salford, Greater Manchester, United Kingdom Buissink C, Department of Medical Imaging and Radiation Therapy, Hanze University of Applied Sciences, Groningen, The Netherlands Aandahl I, Department of Life Sciences and Health, Oslomet, Oslo, Norway Jorge J, Haute Ecole de Sante Vaud – Filie TRM, University of Applied Sciences and Arts of Western Switzerland, Lausanne, Switzerland O’Conner M, University College Dublin, Dublin, Ireland
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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 | 0.009 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.083 | 0.047 |
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".