Prevalence of Cancer Among Individuals Practicing Fasting
Bibliographic record
Abstract
This systematic review aims to comprehensively evaluate the prevalence of cancer among individuals practicing fasting, including intermittent, religious, and therapeutic fasting. The review seeks to synthesize existing epidemiological evidence to determine whether fasting practices are associated with differences in cancer occurrence within human populations. The project follows the PRISMA 2020 guidelines and is registered on OSF for methodological transparency. Literature searches will be conducted across major scientific databases, including PubMed, Scopus, Web of Science, Embase, and Google Scholar, using well-defined search terms related to fasting and cancer prevalence. Eligible studies will include observational and interventional designs reporting quantitative data on cancer prevalence or incidence among fasting populations. Two independent reviewers will perform screening, data extraction, and quality assessment using standardized tools such as the Newcastle–Ottawa Scale and the Cochrane Risk of Bias Tool. Data will be synthesized narratively and, if feasible, quantitatively (meta-analysis) using RevMan software. The expected outcomes include identifying patterns of association between fasting and cancer prevalence, highlighting regional or demographic variations, and recognizing research gaps for future studies. Ultimately, this review will contribute to a clearer understanding of how fasting behaviors may relate to cancer epidemiology, supporting evidence-based recommendations in preventive and public health contexts.
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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 | 0.053 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.007 |
| Bibliometrics | 0.009 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".