Access, excess, and overdiagnosis: the case for thyroid cancer
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
The incidence of thyroid cancer in women is increasing at an epidemic rate. Numerous studies have proposed that the cause is increasing detection due to availability and use of medical diagnostic ultrasound. Our objective was to compare rates of diagnosis across different health-care regions to rates of diagnostic tests and to features of both health and access of the regional populations. This is a population-based retrospective ecological observational study of 12,959 patients with thyroid cancer between January 1, 2000 and December 31, 2008 in Ontario Canada based on the health-care utilization regions (Local Health Integration Networks) of the province of Ontario Canada. We found that some regions of Ontario had four times the rates of diagnosis of thyroid cancer compared to other regions. The regions with the highest use of discretionary medical tests (pelvic ultrasound, abdominal ultrasound, neck ultrasound, echocardiogram, resting electrocardiogram, cardiac nuclear perfusion tests, and bone scan), highest population density, and better education had the highest rates of thyroid cancer diagnoses. Differences in the rates of the ordering of discretionary diagnostic medical tests, such as diagnostic ultrasound, in different geographic regions of Ontario lead to differences in the rates of diagnosis of thyroid cancer.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it