Canola oil compared with sesame and sesame‐canola oil on glycaemic control and liver function in patients with type 2 diabetes: A three‐way randomized triple‐blind cross‐over trial
Bibliographic record
Abstract
BACKGROUND: This study aimed to compare the effects of sesame (SO), canola (CO), and sesame-canola (SCO: a blend) oils on glycaemic control markers and liver function enzymes in adults with type 2 diabetes. METHODS: In this randomized, triple-blind, three-way, cross-over clinical trial, participants replaced their usual oil with the intervention oils for 9 weeks. Serum fasting blood sugar, fasting serum insulin (FSI), insulin resistance (HOMA2-IR), beta-cell function (HOMA2-%B), insulin sensitivity (HOMA2-%S), quantitative insulin sensitivity check index (QUICKI), as well as serum liver function enzymes were measured at baseline and end of intervention periods. RESULTS: Ninety-two participants completed all treatment periods. After adjusting for confounders, all treatment oils resulted in significant improvements in FSI and HOMA2-%S (p < 0.05). SO and SCO led to favourable changes in HOMA2-IR and QUICKI (p < 0.05). Following CO and SCO, there was a significant decrease in HOMA2-%B (p < 0.05). The sex-stratified analysis revealed that FSI and HOMA2-IR were decreased after SO compared to CO in males (p = 0.024). Serum gamma-glutamyltransferase (GGT) was significantly lower following SO compared to CO in females (p = 0.02), however, the difference in change values was not significant (p = 0.058). CONCLUSIONS: SO consumption appears to improve glycaemic control markers in males and serum GGT in females compared with CO in patients with type 2 diabetes (registration code: IRCT2016091312571N6).
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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".