Sensitivity and Specificity of Community Fecal Immunotesting Screening for Colorectal Carcinoma in a High-Risk Canadian Population
Bibliographic record
Abstract
CONTEXT: Community-based programs are a common way of promoting colorectal cancer screening by primary care physicians. Fecal immunochemical testing (FIT) is a screening method commonly used in such programs. Fecal immunochemical testing has advantages to the patient as well as to clinical laboratories. OBJECTIVE: To assess the operational test characteristics of a FIT pilot program in Calgary, Alberta, Canada, between April 2011 and May 2012. DESIGN: Four hundred fifty-seven high-risk patients undergoing both FIT and colonoscopy were included. Areas under the curve and positive predictive values were derived for FIT values and biopsy-proven neoplasia. Subgroup analysis was also performed on men and women and for ages older and younger than the mean age of 62 years. RESULTS: For colorectal carcinoma and colonic adenomas the areas under the curve were 0.79 (95% confidence interval 0.71-0.87) and 0.60 (95% confidence interval 0.54-0.65), respectively. The positive predictive value of a positive FIT result for any neoplasia was 53%. The overall performance of the test for all neoplasia was better for men and better for older individuals. CONCLUSIONS: The performance of FIT in this clinical setting was very good for detecting carcinoma, but marginal for detection of colonic adenomas.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".