IDDF2021-ABS-0178 Association of coffee intake with reduced cancer-related mortality among diagnosed patients with colorectal cancer: a systematic review and meta-analysis
Bibliographic record
Abstract
Background Colorectal cancer (CRC) is the third most common cancer and the fourth most common cause of cancer mortality globally. Recent advances have in diagnostic and treatment options have reduced CRC-related mortality. Coffee consumption has been studied to decrease mortality in colon, liver and pancreatic cancer. Methods A comprehensive, computerized literature search from the electronic database of MEDLINE, Google Scholar, Cochrane Library and OVID was performed with the following search terms: colorectal cancer, coffee, and mortality. Three cohort studies were selected and validated using the Newcastle-Ottawa criteria. Multivariate results were combined under a random-effects model using pooled adjusted hazards ratio (HR). The Cochrane Review Manager Software version 5.4 was used for all analyses. Results Three cohort studies comprising of 3723 patients were analyzed by pooling adjusted hazards ratio using the random-effects model. Coffee consumption was beneficial in reducing cancer-related mortality among CRC patients. Consumption of four or more cups of coffee per day resulted in a decrease in CRC-related mortality (RR 0.59, 95% CI 0.46-0.76, I2 =0%) (IDDF2021-ABS-0178 Figure 1. Forest Plots - Four or More Cups of Coffee Per Day vs Non-Coffee Drinkers). Consumption of 2-3 cups per day was showed to also reduce cancer-related mortality among colorectal cancer patients (RR 0.77, 95% CI 0.66-0.91, I2 = 0%) (IDDF2021-ABS-0178 Figure 2. Forest Plots – Two to Three Cups of Coffee Per Day vs Non-Coffee Drinkers). Conclusions Coffee consumption is beneficial in reducing cancer-related mortality among diagnosed patients with 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 | 0.010 | 0.022 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.018 | 0.032 |
| Bibliometrics | 0.006 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.014 | 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".