P02.95. Treating type 2 diabetes: a cross-sectional audit of naturopathic standards of care using the Naturopathic Patient Database
Bibliographic record
Abstract
Cases of T2DM from the RSNC reported in the NPD were extracted based on an ICD-10 code assessment of E11 (non-insulin-dependant diabetes mellitus). One auditor reviewed 30 files and tabulated audit scores. The Research Ethics Board of CCNM provided ethical oversight of this project. The American Diabetes Association 2010 standards of medical care in diabetes were used as guidelines for the audit. Multiple categories in diagnosis, physical exam, labs, and management were graded on a 0-2 scale. The Measure Yourself Medical Outcome Profile (MYMOP) is used by the RSNC as a universal outcome measure of effectiveness of individualized patient-defined symptoms and was incorporated into the audit and reporting of results. The average audit score is 55.5/90. The most common interventions being used are diet and aerobic exercise, followed by supplements (omega-3 fatty acids) and botanicals. Preliminary data suggests that the standards of care for T2DM are not followed stringently, particularly with regards to complete physical exams, appropriate referrals, and goal-setting. 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 | 0.014 | 0.046 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.006 | 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".