Use and overuse of diagnostic neck ultrasound in Ontario: Retrospective population-based cohort study.
Bibliographic record
Abstract
OBJECTIVE: To provide an overview of the use and possible overuse of diagnostic neck ultrasound (DNUS) by describing and comparing both the ordering rates and the downstream results of DNUS by regions across Ontario. DESIGN: Retrospective population-based cohort study based on electronic health care data. SETTING: Ontario. PARTICIPANTS: Ontario residents (adults aged > 18 years) who had a diagnosis of thyroid cancer between October 1, 1999, and June 30, 2014, and residents who had a DNUS in 2012. MAIN OUTCOME MEASURES: Proportion of Ontario residents in each sub-Local Health Integration Network (LHIN) group who had their first DNUS in 2012 and went on to other relevant tests, diagnoses, and surgery. The sub-LHIN groups were based on increasing age- and sex-adjusted rates of first DNUS. RESULTS: There were 77 238 DNUS tests in 2012 and there was a 7.4-fold variation in the rate of test ordering across the sub-LHIN populations leading to variable rates of actual disease, suggesting screening or uncertain indications for tests. CONCLUSION: Across Ontario, the indications for ordering DNUS are variable, and screening or testing without indication might be a common practice. Establishing effective guidelines for the ordering of DNUS would potentially reduce costs and ultimately reduce the rates of thyroid cancer.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| 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) | 0.001 | 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".