Access to diagnostic imaging and incidental detection of differentiated thyroid cancer in Ontario: A population-based retrospective cohort study
Bibliographic record
Abstract
Global increases in thyroid cancer incidence (≥90% differentiated thyroid cancers; DTC) are hypothesized to be related to increased use of pre-diagnostic imaging. These procedures can detect DTC during imaging for conditions unrelated to the thyroid (incidental detection). The objectives were to evaluate incidental detection of DTC associated with standardized, regional imaging capacity and drivetime from patient residence to imaging facility (the exposures). We conducted a population-based retrospective cohort study of 32,097 DTC patients in Ontario, 2003-2017. We employed sex-specific spatial Bayesian hierarchical models to evaluate the exposures and examine the adjusted odds of incidental detection by administrative regions. Regional capacities of computed tomography and magnetic resonance imaging scanners are positively associated with incidental detection, but vary by sex. Contrary to hypothesis, drivetimes in urban areas are positively associated with incidental detection. Access to primary care may play a role in several administrative regions with higher adjusted odds of incidental detection.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| 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) | 0.001 | 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".