Interval colorectal cancer rates after Hemoccult Sensa and survival by detection mode for individuals diagnosed with colorectal cancer in Winnipeg, Manitoba
Bibliographic record
Abstract
OBJECTIVE: To assess the performance of the Sensa fecal occult blood test (FOBT) in a population-based screening program. SETTING: Manitoba, Canada. METHODS: This historical cohort study included individuals 52 to 74 years of age diagnosed with colorectal cancer (CRC) from 2008 to 2013. CRCs were categorized by detection following a screening program FOBT (Sensa), non-program FOBT (non-Sensa), or no FOBT. Screening program CRCs were classified as program-detected, interval program, or non-compliant. Logistic regression was used to compare characteristics by detection mode. Cox regression adjusted for lead-time was used to examine the effect of detection mode on survival. RESULTS: 1,498 individuals were diagnosed with CRC; 132 (8.8%) had a screening program FOBT, 626 (41.8%) had a non-program FOBT, and 740 (49.4%) had no FOBT. Of the screening program FOBT CRCs, 72 were program-detected (54.5%), 42 were interval program (31.8%), and 18 were non-compliant (13.6%). Sensa interval cancer rate was 37.4% and sensitivity was 63.1% (95% Confidence Interval (CI): 54.3%-72.0%). The risk of death for individuals that had a non-program (Hazard ratio (HR) = 0.57, 95% CI:0.44-0.75) or a screening program FOBT (HR = 0.55, 95% CI:0.31-0.97) was lower than no FOBT. There was no significant difference in the risk of death for interval, non-compliant, and non-program CRCs compared to program-detected CRCs. Adjusting for lead time bias, sex, income quintile, tumour location, and age at diagnosis did not appreciably change the risk estimates. CONCLUSION: More than one-third of CRCs may not be detected by Sensa. There may be no difference in survival between CRC detected by Sensa and non-Sensa FOBTs.
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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 | 0.000 |
| 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 | 0.000 |
| 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".