Does Fecal Occult Blood Testing Really Reduce Mortality? A Reanalysis of Systematic Review Data
Bibliographic record
Abstract
INTRODUCTION: Colorectal cancer (CRC) is a common cause of cancer mortality. A variety of CRC screening strategies are being adopted in many developed countries. Fecal occult blood testing (FOBT) is one option for screening that has the most evidence for efficacy and is also the cheapest approach. Systematic reviews suggest that FOBT is effective in reducing CRC mortality but the data on overall mortality from any cause has rarely been synthesized. METHODS: Randomized controlled trials identified by a Cochrane review of the efficacy of FOBT were reanalyzed. Trials that reported on biennial FOBT with all cause mortality assessed at similar follow-up periods were analyzed. CRC, non-CRC, and all cause mortality were evaluated using a random effects model. RESULTS: Three trials were analyzed, involving 245,217 subjects with 2,148 CRC deaths after almost 3 million patient-years follow-up. The relative risk (RR) of CRC death in the FOBT arm was 0.87 (95% CI = 0.8-0.95). The RR of non-CRC death in the FOBT group was 1.02 (95% CI = 1.00-1.04, p = 0.015). The increase in non-CRC in the FOBT group balanced the decrease in CRC mortality with no overall impact on mortality (RR of dying in the FOBT arm = 1.002, 95% CI = 0.989-1.015). CONCLUSION: The impact of FOBT in reducing mortality from any cause is uncertain and efficacy of this strategy for CRC screening needs reevaluation.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.042 | 0.162 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.016 | 0.030 |
| Bibliometrics | 0.027 | 0.026 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".