Chronic Disease Prevention and Management: Some Uncomfortable Questions
Bibliographic record
Abstract
Morgan, Zamora and Hindmarsh make a compelling case for a national strategy on chronic disease prevention and management. The truths raised in the lead paper are not particularly inconvenient, but they do raise a number of uncomfortable questions: (1) Why are physicians not taking a more responsible and active role to prevent and manage chronic diseases on behalf of their patients? (Physicians must recognize that it is their professional responsibility and their job to provide their patients with the appropriate level of care for chronic conditions.) (2) Why are non-physician healthcare providers not playing a larger role to prevent and manage chronic diseases? (3) Why is there a greater focus on managing chronic diseases than on preventing or delaying them from happening? (4) Have we forgotten the profound impact of the social determinants of health on illness, life expectancy and death?
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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 | 0.019 | 0.068 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.009 | 0.011 |
| Scholarly communication | 0.007 | 0.012 |
| Open science | 0.005 | 0.004 |
| Research integrity | 0.069 | 0.084 |
| Insufficient payload (model declined to judge) | 0.007 | 0.006 |
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".