Multifaceted Effects of Intermittent Fasting on the Treatment and Preventionof Diabetes, Cancer, Obesity or Other Chronic Diseases
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
BACKGROUND: Obesity and diabetes are global epidemics resulting in a range of comorbidities. Both have been linked to an increased risk of hormonal imbalance, cancer, and other significant disorders, which are a concerning trend for cancer rates in the backdrop of rising obesity and diabetes rates worldwide. Around 1 in 10 persons in the United States and Canada have serious illnesses correlated to type 2 diabetes and early death. It is believed that the US economy alone spends $245 billion annually due to this health burden. Lifestyle modification with intermittent fasting protocol and proper diet helps lower blood glucose level, maintain the body mass index, and reduce inflammation, which is the main cause of all chronic diseases. METHODS: We searched case series and clinical trials on type 2 diabetes, insulin resistance, cancer, thyroid, cardiovascular disease, or other inflammatory diseases in response to intermittent fasting in the PubMed, MEDLINE, and Google Scholar databases. OBJECTIVE: In this review, we have focused on intermittent fasting-based approaches that are becoming more widely accepted for improving health and reducing unwanted effects in patients with type 2 diabetes, cancer, cardiovascular disease, neurodegenerative disease, obesity, thyroid, and hormonal imbalance; it is also contemplated whether intermittent fasting can be considered as a non-medicinal therapeutic option for persons suffering from chronic diseases. CONCLUSION: Intermittent fasting successfully reversed diabetes, thyroid, and high blood pressure, elevated lipid levels, and maintained the body mass index; also, studies have shown that it has been instructed to be followed for the treatment and prevention of cancer and neurodegenerative diseases.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.005 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it