Future Supply of Medical Radioisotopes for the UK Report 2014
Bibliographic record
Abstract
The UK has no research nuclear reactors and relies on the importation of 99Mo and other medical radioisotopes (e.g. Iodine-131) from overseas (excluding PET radioisotopes). The UK is therefore vulnerable not only to global shortages, but to problems with shipping and importation of the products. In this context Professor Erika Denton UK national Clinical Director for Diagnostics requested that the British Nuclear Medicine Society lead a working group with stakeholders including representatives from the Science & Technology Facilities Council (STFC) to prepare a report. The group had a first meeting on 10 April 2013 followed by a working group meeting with presentations on 9th September 2013 where the scope of the work required to produce a report was agreed. The objectives of the report are: to describe the status of the use of medical radioisotopes in the UK; to anticipate the potential impact of shortages for the UK; to assess potential alternative avenues of medical radioisotope production for the UK market; and to explore ways of mitigating the impact of medical radioisotopes on patient care pathways. The report incorporates details of a visit to the Cyclotron Facilities at Edmonton, Alberta and at TRIUMF, Vancouver BC in Canada by members of the report team.
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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 | 0.004 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.001 |
| Insufficient payload (model declined to judge) | 0.052 | 0.026 |
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".