Insulin counter-regulatory factors, fibrinogen and C-reactive protein during olanzapine administration: effects of the antidiabetic metformin
Bibliographic record
Abstract
In this study, the Authors assessed some insulin counter-regulatory factors, fibrinogen and C-reactive protein after olanzapine administration, and the effect of metformin on these variables, 37 patients with chronic schizophrenia were given olanzapine (10 mg/day for 14 weeks). Nineteen patients received metformin (850-2550 mg/day) and 18 received placebo in a randomized, double-blind protocol. The following variables were quantified before and after olanzapine: cortisol, leptin, tumor necrosis factor-alpha, glucagon, growth hormone, fibrinogen and C-reactive protein. Results were correlated with the changes in body weight and the insulin resistance index. We have reported elsewhere that metformin did not prevent olanzapine-induced weight gain, and the insulin resistance index significantly decreased after metformin and placebo; Baptista T, et al. Can J Psychiatry 2006; 51: 192-196. Cortisol, tumor necrosis factor-alpha and fibrinogen levels significantly decreased in both groups. Glucagon significantly increased after metformin (P=0.03). Leptin tended to increase after placebo (P=0.1) and displayed a small nonsignificant reduction after metformin. The C-reactive protein did not change significantly in any group. Contrarily to most published studies, olanzapine was associated with decreased insulin resistance. Decrements in cortisol, fibrinogen and tumor necrosis factor-alpha levels point to an improvement in the metabolic profile. The trend for leptin to increase after placebo, but not after metformin in spite of similar weight gain suggests a beneficial effect of this antidiabetic agent.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".