Treating Type 2 Diabetes: A Cross-sectional Audit of Naturopathic Standards of Care Using the Naturopathic Patient Database
Bibliographic record
Abstract
The Naturopathic Patient Database is a data management tool developed by the Canadian College of Naturopathic Medicine to collect patient data from its teaching clinic, the Robert Schad Naturopathic Clinic. This study investigated how type 2 diabetes mellitus was managed at the Robert Schad Naturopathic Clinic from May 2009 to February 2011. Cases of type 2 diabetes mellitus from the Robert Schad Naturopathic Clinic reported in the Naturopathic Patient Database were extracted based on an International Classification of Diseases, 10th revision code assessment of E11 (non-insulin-dependent diabetes mellitus) and files were audited. The American Diabetes Association 2010 standards of medical care in diabetes were used as guidelines for the audit. Multiple categories in diagnosis, physical exam, laboratory tests, and management were graded on a 0 to 2 scale. The average audit score was 55.5/90. The most common interventions being used are diet and aerobic exercise, followed by supplements (omega-3 fatty acids) and botanicals. These data suggest that the American Diabetes Association standards of care for type 2 diabetes mellitus are not followed stringently. Education and creation of a naturopathic standard of care may improve audit performance and patient outcomes.
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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 | 0.010 | 0.019 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".