Addressing the Common Pathway Underlying Hypertension and Diabetes in People Who Are Obese by Maximizing Health: The Ultimate Knowledge Translation Gap
Bibliographic record
Abstract
In accordance with the WHO definition of health, this article examines the alarming discord between the epidemiology of hypertension, type 2 diabetes mellitus (T2DM), and obesity and the low profile of noninvasive (nondrug) compared with invasive (drug) interventions with respect to their prevention, reversal and management. Herein lies the ultimate knowledge translation gap and challenge in 21st century health care. Although lifestyle modification has long appeared in guidelines for medically managing these conditions, this evidence-based strategy is seldom implemented as rigorously as drug prescription. Biomedicine focuses largely on reducing signs and symptoms; the effects of the problem rather than the problem. This article highlights the evidence-based rationale supporting prioritizing the underlying causes and contributing factors for hypertension and T2DM, and, in turn, obesity. We argue that a primary focus on maximizing health could eliminate all three conditions, at best, or, at worst, minimize their severity, complications, and medication needs. To enable such knowledge translation and maximizing health outcome, the health care community needs to practice as an integrated team, and address barriers to effecting maximal health in all patients. Addressing the ultimate knowledge translation gap, by aligning the health care paradigm to 21st century needs, would constitute a major advance.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".