Advanced Diagnostic Imaging and Differentiated Thyroid Cancer in Ontario: Detection, Incidence, Access, and Patient Outcomes
Bibliographic record
Abstract
Overdetection of thyroid cancer from increasing use of diagnostic imaging is a leading hypothesis to explain worldwide incidence rate increases in the context of low and relatively stable mortality rates. In this dissertation, I present three studies that investigate factors and outcomes related to use of advanced diagnostic imaging in the incidence of differentiated thyroid cancer (DTC) in Ontario. In the first study, I classified pre-diagnostic pathways with advanced diagnostic imaging procedures as incidentally detected cases (IDCs) (otherwise non-incidentally detected, non-IDCs). I found that a up to 20% and 30% of female and male DTCs were IDCs, and that incidence rate ratios (IRR) for these cases (IRR, females: 3.45, 95% CI 3.00-3.96; males: 3.21, 95% CI 2.65-3.90) increased more rapidly than non-IDCs (IRR, females: 2.49, 95% CI 1.22-5.08; males: 2.38, 95% CI: 2.20-2.58) in the most recent period (2013-2017) compared to earliest (1998-2002). In the second study, I evaluated standardized imaging capacity and geographic access to imaging facilities associated with increasing incidence of IDCs. I found that CT and MRI imaging capacity were associated with higher odds of IDCs among males (odds ratio, OR: 1.26, 95% CI 1.01-1.57) and females (OR: 1.44, 95% CI 1.10-1.85), respectively. Drivetime (per 10 minutes) from patient residence to imaging facility was associated with higher odds of incidental detection of DTC outside rural areas (OR range: 1.06, 95% CI 1.02-1.12 in urban areas to 1.44, 95% CI 1.25-1.66 in large urban areas). In the third study, I used instrumental variable methods to compare five-year mortality and recurrence between IDCs and non-IDCs. While I did not identify a strong instrument—causing imprecise results—the findings show confounding by prognostic and clinical factors which are unavailable in the administrative data. Sensitivity analyses support the design and approach upon identification of a strong instrument. These findings show that use of advanced diagnostic imaging contributes to Ontario’s increases in DTC incidence. As use of advanced diagnostic imaging increases, incidence rates of patients with IDCs will continue increasing. Overall, these findings provide a foundation to conduct future epidemiological, clinical, and health services research of the burden of DTC in Ontario.
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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 | 0.000 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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".