Rates of New or Missed Colorectal Cancer After Barium Enema and Their Risk Factors: A Population-Based Study
Bibliographic record
Abstract
OBJECTIVE: Double-contrast barium enema (DCBE) is widely used in clinical practice to detect colorectal cancer (CRC). Our objective was to evaluate the rate of new or missed CRC following DCBE and the associated risk factors in a population-based study. METHODS: All patients (> or =20 yr old) with a new diagnosis of CRC between April 1, 1997, and March 31, 2004, in Ontario were identified. Data were extracted from the Ontario Health Insurance Program, the Canadian Institute for Health Information, the Registered Persons Database and the Ontario Cancer Registry. Patients who had a DCBE examination 36 months prior to the diagnosis of CRC were divided into two groups: detected cancers (DCBE within 6 months prior to diagnosis) and new or missed cancers (DCBE 6-36 months prior to diagnosis). Multivariate analysis was used to evaluate factors associated with new or missed CRC. RESULTS: We identified 13,849 patients who had a DCBE 36 months prior to the diagnosis of CRC. The overall rate of new or missed cancers following DCBE was 22.4%. Independent risk factors for new or missed cancers were older age, female sex, previous abdominal or pelvic surgery, diverticular disease, right-sided CRC, and having the DCBE in an office setting. CONCLUSIONS: Physicians who use DCBE to evaluate the colon must inform their patients that if a cancer is present, there is an approximately one in five chance that it will be missed. Given the recent endorsement of CT colonography by the U.S. Multi-Society Task Force on Colorectal Cancer as an option for CRC screening, it may be time to reconsider the use of DCBE to detect CRC.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 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) | 0.001 | 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".