CT, MRI and ultrasound scanning rates: Evaluation of cancer diagnosis, staging and surveillance in ontario
Bibliographic record
Abstract
OBJECTIVE: To examine practice patterns and rates of computed tomography (CT), magnetic resonance imaging (MRI), and abdominal ultrasound (AUS) during staging, treatment and surveillance for cancer patients. METHODS: Using Ontario Health Insurance Plan billing data linked to the Ontario Cancer Registry, we determined rates of CT, MRI, and AUS by body site for breast, colorectal, lung, lymphoma, and prostate cancer, from 1998 to 2002. Rates of scans were additionally examined by region of patient residence and time from cancer diagnosis. RESULTS: The frequency of imaging increased in nearly all scans and tumors over the study period. Rates of peri-diagnosis scans varied substantially by region, ranging from 1.7-fold variation (CT for lung cancer) to 50-fold variation (MRI for breast cancer). For breast cancer, there is possible over-utilization of CT, but overall rates of scanning appear reasonable for the other four cancers. CONCLUSIONS: Considerable regional variation in imaging rates suggests utilization guidelines should be developed or knowledge transfer initiatives are needed to improve compliance to existing guidelines. In breast cancer, there appears to be over-utilization of imaging. Further studies are necessary to determine utilization for each stage, the reason scans were obtained, and the impact of scans on patient outcomes.
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.
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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| 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".