Choosing Wisely in oncology: Screening for a new primary cancer in patients with metastatic disease.
Bibliographic record
Abstract
295 Background: The Choosing Wisely Canada (CWC) campaign aims to start conversations about unnecessary treatments and procedures in order to improve quality of care. In particular, the CWC campaign in cancer seeks to reduce interventions that are not supported by evidence and contribute to unnecessary rising costs of cancer care. We sought to document the performance of cancer screening for a new primary cancer in patients with existing metastatic cancer (CWC statement #2). Methods: We used population-based administrative health care databases from Ontario, Canada held at the Institute for Clinical Evaluative Sciences (ICES). The cohort included all adult residents of Ontario of eligible screening age (age 50 or older) diagnosed with incident, stage 4 (metastatic) colorectal cancer (CRC), lung, breast, or prostate cancer between January 1, 2007 and December 31, 2012. We examined screening tests for CRC and breast cancer in the first 1 and 3 years after diagnosis of an unrelated cancer. Given the high mortality rate in this population, screening rates were calculated using the cumulative incidence function which takes into account the competing risk of death or the occurrence of the cancer for which the patient was being screened (prior to being screened). Results: Among the 20,992 patients with stage 4 lung, breast, or prostate cancer, CRC screening within 1 year of cancer diagnosis occurred in 2.8%, 6.1%, and 13.0%, respectively. Within 3 years of diagnosis, screening rates were 3.9%, 11.9%, and 26.9%, respectively. Among the 10,034 women with metastatic CRC or lung cancer, breast cancer screening within 1 year of cancer diagnosis occurred in 8.0% and 8.7% of women, respectively. Within 3 years of diagnosis, screening rates were13.1% and 10.2%, respectively. Screening rates were higher in patients age 50-74 than those ≥75 years. Conclusions: Our findings indicate that up to one quarter of patients with metastatic cancer receive subsequent screening tests for other cancers, which are unnecessary as these patients are unlikely to benefit. Further studies are warranted to examine resource implications, potential patient and societal harms, and the future impact of the CWC campaign on this practice.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".