Screening for a new primary cancer in patients with existing metastatic cancer: a retrospective cohort study
Bibliographic record
Abstract
Background: Cancer screening aims to detect malignant disease early in its natural history when interventions might improve patient outcomes. Such benefits are unclear when screening occurs for patients with an existing high risk of death. Our aim was to study the extent of routine cancer screening for a new primary cancer in patients with existing metastatic cancer. Methods: We used administrative databases from Ontario to identify a retrospective cohort of adults of eligible screening age (≥ 50 yr) who had a diagnosis of stage IV (metastatic) colorectal, lung, breast or prostate cancer between 2007 and 2012. We calculated the cumulative incidence of cancer screening over time for colorectal and breast cancer. Results: Among the 20 992 patients with metastatic lung, breast or prostate cancer, 2.9%, 6.3% and 13.3% of patients, respectively, underwent testing for colorectal cancer within 1 year of cancer diagnosis. Within 3 years of diagnosis, rates reached 4.1%, 12.3% and 27.5%, respectively (8.5% of all patients). Incidence of colorectal cancer testing was higher among patients who received their diagnoses more recently compared with patients with diagnoses from earlier time periods (p = 0.0143). Among the 10 034 women with metastatic lung or colorectal cancer, 8.7% and 8.0% of patients, respectively, underwent breast cancer screening within 1 year of cancer diagnosis. Within 3 years of diagnosis, screening rates reached 10.2% and 13.1%, respectively. Interpretation: Our findings indicate excessive rates of cancer screening among patients with metastatic cancer who are unlikely to benefit. Further studies are warranted to identify predictors for screening, resource implications, potential and real harms borne by patients, and the impact of a recent Choosing Wisely statement recommending against the 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 | 0.003 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".