The CIHR Team in Aboriginal Antidiabetic Medicines: A Community-Based Collaborative Approach Uniting Healers and Biomedical Scientists to Validate Cree Traditional Medicine
Bibliographic record
Abstract
Obesity and Type 2 diabetes are considered as global epidemics by the WHO. Aboriginal populations such as the Cree of Eeyou Istchee (James Bay area of northern Quebec) are particularly affected and suffer greater complications, in part because of the cultural inadequacy of modern pharmaceutical therapies. A multidisciplinary team was therefore put together to explore the antidiabetic potential of Boreal forest plants stemming from Cree Traditional Medicine (TM). The team is composed equally of scientists with expertise in botany, phytochemistry, nutrition, pharmacology, biochemistry, toxicology and clinical endocrinology as well as Cree Elders and members of various Cree health-related institutions, notably including the Cree Board of Health and Social Services of James Bay (CBHSSJB). A novel ethnobotanical approach based on diabetes symptoms was used to identify potential antidiabetic plants. A total of 17 species were characterized phytochemically and screened for primary and secondary antidiabetic activity, toxicological potential and mode of action using a comprehensive platform of bioassays. Most promising species were subjected to bioassay-guided fractionation to identify active principles. Bioavailibility as well as antihyperglycemic and anti-obesity efficacy are then confirmed using in vivo animal models of obesity, insulin resistance or diabetes. Clinical studies are also underway to document the safety and efficacy of selected species using a culturally-adapted, all-inclusive, observational protocol. Finally, our project represents a pilot study for the integration of Cree TM into diabetes care for the CBHSSJB. Funded by the Canadian Institutes of Health Research.
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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 | 0.016 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.009 | 0.004 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.003 | 0.012 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.011 | 0.002 |
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".