The nuclear medicine and molecular medicine podcast. Multimedia continuing professional development
Bibliographic record
Abstract
1051 Learning Objectives 1. Understand what a podcast is. 2. Illustrate the methods to obtain and listen to the podcast. 3. Be aware of specific areas that the podcast covers. 4. Learn about how to contribute to the podcast. 5. Discuss the future of podcasting and how it can be used to meet CPD requirements. Podcasting is a new type of automatic audio or video journal; it is perfectly suited for time effective delivery of information of specialised information to Nuclear medicine professionals. It is simply an audio (or video) file which is automatically sent to your computer, ipod or audio player whenever a new episode is created. Podcasting is an ideal complement / Supplement to traditional journals and conferences. The format is ideal for interviews and can help highlight salient points and direct the listener to published material for follow up. So far, there have been 34 episodes over 4 years with leading nuclear medicine practitioners. Sample episodes: Dr Paganelli, Italy, Breast Isotope therapy Dr Dadachova, USA, Viral Therapy Dr O9Brien, Canada, Molybdenum supply Dr Krishnamurthy, USA, Hepatobiliary Nuclear Medicine Dr Bacj, Sweden, V/Q SPECT. Prof Ben-Haim, UK, D-SPECT Dr Wakers, USA, Myocardial Perfusion Imaging and Diabetes Dr Strauss, USA, vulnerable plaque References: http://nuccast.com/ http://cardiac.nuccast.com/ M Avison, M Burniston 2006, 27(5):475-476 Nucl. Med. Comms. A complimentary internet based journal for the future-will this help support continuing professional development
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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 | 0.001 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.400 | 0.142 |
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".