Antibiotic use and colorectal neoplasia: a systematic review and meta-analysis
Bibliographic record
Abstract
Background and aims Colorectal cancer (CRC) is the third most common cancer for women and men and the second leading cause of cancer death in the USA. There is emerging evidence that the gut microbiome plays a role in CRC development, and antibiotics are one of the most common exposures that can alter the gut microbiome. We performed a systematic review and meta-analysis to characterise the association between antibiotic use and colorectal neoplasia. Methods We searched PubMed, EMBASE, and Web of Science for articles that examined the association between antibiotic exposure and colorectal neoplasia (cancer or adenoma) through 15 December 2019. A total of 6031 citations were identified and 6 papers were included in the final analysis. We assessed the association between the level of antibiotic use (defined as number of courses or duration of therapy) and colorectal neoplasia using a random effects model. Results Six studies provided 16 estimates of the association between level of antibiotic use and colorectal neoplasia. Individuals with the highest levels of antibiotic exposure had a 10% higher risk of colorectal neoplasia than those with the lowest exposure (effect size: 1.10, 95% CI 1.01 to 1.18). We found evidence of high heterogeneity (I2=79%, p=0.0001) but not of publication bias. Conclusions Higher levels of antibiotic exposure is associated with an increased risk of colorectal neoplasia. Given the widespread use of antibiotics in childhood and early adulthood, additional research to further characterise this relationship is needed.
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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 | 0.012 | 0.036 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.018 | 0.037 |
| Bibliometrics | 0.008 | 0.010 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".