OPTIMAX 2018 - a focus on education in radiology
Bibliographic record
Abstract
This year, OPTIMAX was warmly welcomed by \nUniversity College Dublin. For the sixth time \nstudents and teachers from Europe, South Africa, \nSouth America and Canada have come together \nenthusiastically to do research in the Radiography \ndomain. As in previous years, there were several \nresearch groups consisting of PhD-, MSc- and BSc \nstudents and tutors from the OPTIMAX partner \nUniversities or on invitation by partner Universities. \nOPTIMAX 2018 was partly funded by the partner \nUniversities and partly by the participants.<br/> \n \n<br/> \nThis year, five research projects were performed with \na focus on education on dose- and image quality \noptimization.<br/> \n \n<br/> \nThe research projects were:<ul> \n<li>CT Simulation as an Active learning tool</li> \n<li>Redesigning a Radiography Practical Active \nLearning Space</li> \n<li>Does Radiographer Training Across Europe Alter \nImage Viewing Patterns and Decisions?</li> \n<li>An Investigation into the Use of Lead Shielding \nProtection in Abdominal Radiography</li> \n<li>Inter-user Variability in DXA Scanning and \nAnalysis</li> \n \n</ul><br/> \nThe summer school was concluded with a poster \nsession and a conference, where the research \nteams presented their results. All five abstracts were \nsubmitted to the European congress of Radiology \n(ECR) and, when accepted, will be presented by the \nstudents as posters, or oral presentations.<br/> \n \n<br/> \nThis book comprises of two sections, the first section \ncontains several chapters about new educational \napplications for Radiology Education. The second \nsection contains the research papers of the five \nresearch projects.<br/> \n \n<br/> \n<b>Steering committee OPTIMAX 2018</b><br/> \n \n<ul> \n<li>Hogg P, School of Health Sciences, University of \nSalford, Greater Manchester, United Kingdom</li> \n<li>Buissink C, Department of Medical Imaging and \nRadiation Therapy, Hanze University of Applied \nSciences, Groningen, The Netherlands</li> \n<li>Aandahl I, Department of Life Sciences and \nHealth, Oslomet, Oslo, Norway</li> \n<li>Jorge J, Haute École de Santé Vaud – Filiè TRM, \nUniversity of Applied Sciences and Arts of \nWestern Switzerland, Lausanne, Switzerland</li> \n<li>O’Conner M, University College Dublin, Dublin, \nIreland</li> \n</ul>
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 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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".