Does acetylsalicylic acid or warfarin affect the accuracy of fecal occult blood tests?
Bibliographic record
Abstract
BACKGROUND: Current guidelines for screening of colorectal cancer do not offer specific recommendations for cessation of antithrombotic agents prior to fecal occult blood test (FOBT). AIM: To asess the accuracy of FOBT in patients taking acetylsalicylic acid (ASA) or warfarin. METHODS: A literature search was conducted for studies that investigated the accuracy of FOBT in patients taking ASA and warfarin. The primary outcome was the pooled relative risk (RR) for true positive FOBT for detecting significant colonic neoplasia in patients taking ASA or warfarin compared with controls. The secondary outcome was a pooled RR for true positive in guaiac FOBT (g-FOBT) compared with immunochemical FOBT (i-FOBT). RESULTS: Five observational studies included 759 patients taking ASA and 1652 control subjects. In patients taking ASA, pooled RR for true positive FOBT was 0.82 (95% confidence interval [CI] 0.73-0.93, P=0.0009), pooled RR for true positive g-FOBT was 0.69 (95% CI 0.60-0.79, P<0.0001), whereas pooled RR for true positive i-FOBT was 1.013 (95% CI 0.81-1.30, P=0.8182). Five observational studies included 806 patients taking warfarin and 10 338 control subjects. In patients taking warfarin, pooled RR for true positive FOBT was 1.559 (95% CI 1.349-1.801, P<0.0001). CONCLUSION: The results of our meta-analysis demonstrate that in patients taking ASA, there is a decrease in the positive predictive value (PPV) of g-FOBT but no significant difference in the PPV of i-FOBT compared with control subjects for detecting significant neoplasia. In patients taking warfarin, the PPV of FOBT was increased for detection of colorectal cancer compared with control subjects.
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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 | 0.028 | 0.147 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.007 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 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".