Bibliographic record
Abstract
Abstract Diabetes is a major deadly disease. In 2019 alone, it caused an estimated 1.5 million deaths world-wide. Cases of diabetes are rising rapidly in low- and middle-income countries. Natural remedies that can lower the glucose level would be very useful, particularly to people living in low- and middle-income countries. A 2-year case study was carried out, therefore, to determine if Nigella sativa tea can lower the glucose level in a 72-year-old man with type-2 diabetes, stage 3–4 chronic kidney disease, and congestive heart failure. Changes in body weight, lipids, estimated glomerular filtration rate (eGFR), and urinary albumin-to-creatinine ratio (UACR) were also studied. N. sativa tea was prepared with N. sativa, barley, and wheat seeds. The 72-year-old drank approximately 50 ml of N. sativa tea daily, in the morning. Results showed that after drinking N. sativa tea daily, hypoglycemia started to occur and occurred more frequently as time went by and that the glycated hemoglobin, HbA1c, was decreasing. Subsequently, the dosages of insulin glargine and insulin aspart were reduced by 33% and 50%, respectively. Results also showed that weight loss led to the 72-year-old cutting back his intake of the diuretic furosemide by at least 50%. His triglycerides level was also lower and there were no changes in his total cholesterol, high-density lipoprotein cholesterol, and low-density lipoprotein cholesterol levels. His eGFR was stable but his UACR was worsening. N. sativa tea is easy to prepare, costs very little, and could be a natural remedy for mitigating diabetes and edema. Many more studies on N. sativa are warranted.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.005 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".