Association between folate intake and risk of head and neck squamous cell carcinoma
Bibliographic record
Abstract
The results of published studies about the relationships between folate intake and risk of head and neck squamous cell carcinoma (HNSCC) remained inconsistent. Hence, a comprehensive and dose-response meta-analysis was performed to clarify the association between folate intake and HNSCC risk.The electric searches of Pubmed, Medline, and EMBASE databases were performed to identify the studies examining the relationship between folate intake and HNSCC risk on April 5, 2017. According to the inclusion criteria, finally 9 studies were included in this meta-analysis. The pooled odds ratios (ORs) with 95% confidence intervals (CIs) were used to evaluate the strength of associations. Dose-response analysis was conducted to quantitate the relationship between dietary folate intake and HNSCC risk.The pooled OR for assessing the risk of HNSCC and folate intake in the highest level versus lowest level was 0.505 (95% CI 0.387-0.623). The linearity model of dose-response analysis indicated that with increased 100 μg/d folate intake, the risk of HNSCC decreased 4.3% degree (OR 0.957, 95% CI 0.935-0.980).These results indicate that folate is a protective nutrient against HNSCC carcinogenesis.
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 | 0.004 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.008 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".