Research Proposal Dalhousie Medical School Summer Studentship 2010 Title: Evaluation of Combined SPECT-CT to Assess Coronary Artery Calcium
Bibliographic record
Abstract
Coronary Artery Disease (CAD) is a major medical condition, and indeed is the leading cause of death in western society. A number of methods exist to establish an individual’s risk of having CAD and to assess potential outcome. Myocardial Perfusion Imaging (MPI) is a nuclear medicine technique in which a radioactive tracer is injected under rest and stress conditions, followed by imaging of the heart, to assess cardiac perfusion. One of the primary applications of MPI is the assessment of whether a patient has CAD. Approximately 10 MPI studies per day (2500 per year) are performed at the QEII. MPI is performed using a Single Photon Emission Computed Tomography (SPECT) scanner. Coronary artery calcium (CAC) scoring is a relatively new method of assessing risk of cardiac events by assessing the extent and severity of coronary artery calcification 2. CAC scoring was originally performed using electron beam tomography, but this technology is not widely available and more recently CAC scoring has been performed with standard computed tomography (CT) scanners. Studies have shown generally increasing cardiac risk with increasing CAC. When CAC is zero or very low, there is a very low probability of cardiac mortality in the follow-up period. However, CAC is not entirely specific, and adoption of this technique has not been universal. Indeed, it is not currently done at the QEII.
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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 | 0.008 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.005 | 0.003 |
| Insufficient payload (model declined to judge) | 0.282 | 0.103 |
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".