Nut Intake and Risk of Cancer and its Mortality: a Study Protocol for a Systematic Review and Dose-Response Meta-analysis of Observational Studies
Bibliographic record
Abstract
Background: This study protocol outlines the planned, systematic review and dose-response meta-analysis of nuts intake with cancer risk and its mortality. Methods: This meta-analysis will be done based on the Preferred Reporting Items for Systematic Reviews and Meta-Analysis Protocols (PRISMA-P). A systematic literature search will be conducted using online databases, including PubMed/Medline, ISI Web of Science, and Scopus with no limitation in language or time of publication to identify observational studies investigating the association of nuts intake with cancer risk and its mortality. The target population will be adults (≥18 years of age). Random-effects models will be used to calculate pooled effect sizes (ESs) for the risk of cancer and its mortality based on the comparison between the highest and lowest categories of nut intake and to incorporate variation between studies. Linear and non-linear dose-response analyses will be done to evaluate the dose-response associations between nut intake and risk of cancer and its mortality. The Newcastle-Ottawa Scale (NOS) will be used to assess the risk of bias or quality of included studies. Conclusion: The findings of this systematic review and dose-response meta-analysis will summarize all available evidence on the association between nut intake and risk of cancer and its mortality.
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 | 0.103 | 0.153 |
| Meta-epidemiology (narrow) | 0.006 | 0.006 |
| Meta-epidemiology (broad) | 0.015 | 0.027 |
| Bibliometrics | 0.010 | 0.009 |
| Science and technology studies | 0.004 | 0.004 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.006 | 0.004 |
| Research integrity | 0.009 | 0.008 |
| Insufficient payload (model declined to judge) | 0.053 | 0.008 |
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".