Low serum 25-hydroxyvitamin D [25(OH)D] concentrations in type 2 diabetes mellitus patients presenting to a functional medicine clinic
Bibliographic record
Abstract
This study explores the relationship of 25-hydroxylvitamin D blood levels in 106 randomly selected patient files with diagnosed type 2 Diabetes Mellitus (t2DM) who enrolled in a functional medicine diabetes reversal program from a chiropractic clinic located in Annapolis, Maryland, USA. Using a conservative recommendation for normal serum 25-hydroxyvitamin D concentration of 32 ng/ml, insufficiency level of 20 - 30 ng/ml, and deficiency level et al. (2011) determined the optimal concentration of serum 25OHD to be 40 ng/ml in order to optimize insulin sensitivity. In our sample 100/ 106 (94%) had vitamin D levels at or below this optimal cut-off level. BMI was negatively correlated with vitamin D; that is, the greater the BMI of the patient the less their vitamin D level. Both obesity and hypovitaminosis D are each mutually exclusive predictors for t2DM. Obesity and vitamin D deficiency may work synergistically to propel an individual into the diseased state of t2DM. As this study demonstrates that the majority of people with t2DM suffer from inadequate amounts of vitamin D, vitamin D testing should be routine for all people at risk for t2DM, prediabetics and those currently suffering with t2DM in order to elevate levels sufficiently to improve insulin sensitivity and improve long-term 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 | 0.000 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".