Bibliographic record
Abstract
PURPOSE: Diabetic patients with co-morbid mental illness are commonly encountered in clinical practice. Not only are diabetes and mental illness both common in the general population, but rates of diabetes are significantly higher in individuals with psychiatric disorders. This paper reviews literature related to the interplay between these pathologies and the consequent clinical challenge faced by physicians. METHODS: A systematic review was conducted, examining specific aspects of psychiatric illness which may affect diabetic outcomes. RESULTS: Decreased adherence is a feature of many psychiatric conditions, and can have a major effect on diabetic management and development of long term complications. Glycemic regulation may also be complicated by physiologic changes affecting carbohydrate metabolism. Patterns of counter-regulatory hormone secretion are altered in many psychiatric conditions, which may necessitate an altered diabetic treatment regimen. Further difficulties arise as many psychiatric medications have adverse metabolic effects. CONCLUSIONS: Diabetic patients with mental illness present a unique clinical challenge as a result of issues related to behaviour, physiology and medications. Clinicians should be able to recognize "problem patients" who may in fact have undiagnosed, treatable, psychiatric pathology. In patients carrying existing diagnoses, complicating factors to diabetic control should be recognized, and steps taken to minimize adverse effects.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 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".