The Development of a PET/CT Program \nin Newfoundland and Labrador
Bibliographic record
Abstract
Positron emission tomography (PET) is a \ntype of Nuclear Medicine (NM) imaging \ntechnology that allows true imaging of \nhuman physiological and biochemical \nprocesses. \nAt present, patients in Newfoundland \nand Labrador who require a PET scan \nmust travel out of the \nprovince to either \nAlberta or Quebec at a \nsubstantial cost to the \nprovincial health system \nas well as to the patient \nand his/her family. \nWhile the recorded number of NL \nresidents who received PET scans in the \npast has been relatively small (fewer than \n35 patients per year since 2004), these \nnumbers may not represent the true \nsize of the population that might have \nbenefited from PET nor provide a reliable \nguide to future demand. \nToday, PET scanners are most often \navailable only as ‘hybrid’ models that \ncombine PET with computed tomography \n(CT). When PET is combined with \nCT, the fused images allow accurate \nsimultaneous visualization of function \nor physiology (in the PET element) and \nanatomy or structure (in the CT element). \nA technology closely associated with \nPET scanning is a medical \ncyclotron; Locating a PET \nscanner close to a cyclotron \nis important for its clinical and \nresearch utility. \nThis report was initially \ndesigned to examine researchbased \nevidence about whether the \nprovince of NL should acquire a PET \nscanner. Given that the Government \nof NL has announced its intention to \npurchase a PET scanner and a medical \ncyclotron, and that a further decision \nhas been reached to locate both pieces \nof equipment in St. John’s, our focus has \nbeen on a set of ancillary, but still very \nimportant, issues that contribute to the \nprimary research question, below.
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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 | 0.005 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.034 | 0.009 |
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".