Coffee is protective against oral and pharyngeal cancer: A systematic review and meta-analysis
Bibliographic record
Abstract
OBJECTIVES: Coffee is one of the most popular and consumable drinks worldwide. However, there are conflicting results on the influence of this drink in oral and pharyngeal cancer risk. To clarify this, we aimed to systemically review and carry out a meta-analysis of the relevant literature on the association between coffee and oral and pharyngeal cancer. STUDY DESIGN: We carried out an electronic search of publications up to August 2016 from PubMed, National Library of Medicines Medline, Embase, Science Direct and the Cochrane Central Register. The Newcastle-Ottawa scale was used to address the quality of the studies a meta-analysis was carried out using random-effects models. RESULTS: From the 22,515 entries identified in the search, 13 case-control and 4 cohort studies were selected. With regards to quality on the Newcastle-Ottawa scale, an overall value of 6.06 was obtained. The analysis for oral and pharyngeal cancer grouped together indicated a pooled OR of .69 (95% CI of .57-.84; p<.001) for high versus low coffee consumption with a moderate heterogeneity (I2: 50.3%; p=.009). Regarding studies on oral cavity cancers we observed a pooled OR of 0.82; 95% CI =.58-1.16; p=.257) and for pharyngeal cancers a pooled OR of .72 (95% CI of 0.54-.95; p=.019). There was no significant publication bias. CONCLUSION: The results show an inverse association between high coffee consumption and the risk of oral and pharyngeal cancers, which indicates that coffee may have a protective role against these cancers. Further larger prospective observational cohort studies are needed to address any effect of other possible co-factors.
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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 | 0.029 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.017 | 0.039 |
| Bibliometrics | 0.007 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 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".