REALIZATION OF A CANINE POSITIONING DEVICE FOR IN SITU PROSTATE PHASE CONTRAST – COMPUTED TOMOGRAPHY IMAGING
Bibliographic record
Abstract
Background: Worldwide, prostate cancer (PCa) is the most commonly diagnosed non-skin cancer in men.The current diagnostic standard of PCa requires invasive procedures such as needle biopsies.Non-invasive medical imaging techniques, such as Computed Tomography (CT), are only used as an adjunct for staging PCa.The development of a novel non-invasive imaging technique for PCa could revolutionize diagnostic standards and improve patient prognosis.The similarity between canine and human prostates, as well as similar PCa pathophysiology, makes the dog an ideal model for human PCa research.Initial investigations with Phase Contrast -CT (PC-CT) has shown potential for detecting morphological abnormalities in ex vivo canine prostates and therefore warrants further testing as a potential PCa diagnostic imaging technique.This research addresses the design, development and implementation of a canine positioning device used for in situ prostate PC-CT imaging on the Biomedical Imaging and Therapy -Insertion Device Beamline at the Canadian Light Source.This device is currently being used to collect micron-level resolution PC-CT reconstructions of canine cadaver prostates.This thesis lays the ground work for canine imaging on the BMIT -ID beamline at the CLS.The design and implementation of the device are described, along with the issues discovered and addressed.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".