The Effect of Intermittent Fasting on Glucose and Lipid Disorder among patients of Diabetes Mellitus Type-2
Bibliographic record
Abstract
Background: The diabetes Mellitus Type -2 disease has become prevalent globally and the treatment of the disease is quite expansive and long term, especially in the low-income countries like Pakistan. Aim: To explore the evidence of the efficacy of Intermittent Fasting as an alternative therapy in Diabetes Mellitus Type-2 by reviewing the existing literature on intermittent fasting globally. Methods: The literature on the effect of Intermittent Fasting on diabetes type-2 was searched on PubMed, and Google scholar and more than 20 studies conducted on the IF on human beings were identified at national, regional, and global levels and reviewed. Results: Not much literature is available on Intermittent Fasting, especially in low-income countries and the majority of the studies have been conducted in high-income countries like the USA, Canada, Australia, and the UK. A few long-term, Randomized Control Trials have been conducted, and most are short-term studies. A few studies have been found on Diabetes Mellitus Type-2 in India and Pakistan that too related to the prevalence and economic burden of the disease in these countries. Conclusion: Based on the studies reviewed, we can conclude that there is growing evidence demonstrating the benefits of Intermittent Fasting in short- and medium-term studies on glucose and lipid homeostasis but there is a need to carry out more long-term studies with a larger number of participants and in low-income countries. Furthermore, the existing literature reveals that Intermittent Fasting can be used as an alternative in the supervision of physicians otherwise can be counterproductive. Keywords: Intermittent Fasting, Diabetes mellitus, weight loss, Lipid Disorder
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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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".