Protocol for Psychopharmacologic Management of Behavioral Health Comorbidity in Adult Patients with Diabetes and Soft Tissue Infections in a Tertiary Care Hospital Setting
Bibliographic record
Abstract
GENERAL PURPOSE: To provide information about the effect of psychiatric comorbidities on wound healing in patients with diabetes mellitus (DM). TARGET AUDIENCE: This continuing education activity is intended for physicians, physician assistants, nurse practitioners, and nurses with an interest in skin and wound care. LEARNING OBJECTIVES/OUTCOMES: After participating in this educational activity, the participant should be better able to:1. Discuss the connection between DM and the development of psychiatric comorbidities.2. Identify the drugs recommended in the treatment of these psychiatric comorbidities.3. List cautions and contraindications related to the drugs discussed. ABSTRACT: In patients with diabetes mellitus type 2, psychiatric comorbidities such as depressive and anxiety disorders are 60% or more prevalent than in the general population. The severity of mental illness and the duration of diabetes have been shown to correlate with worsening glycemic control, thus impeding wound healing. A retrospective chart review was conducted in all patients with diabetes mellitus admitted to the wound service with prior or current psychiatric symptoms of anxiety, depression, or cognitive impairment. A psychopharmacologic protocol was developed based on the clinical data collected and treatment parameters used by the behavioral health consultation liaison service.
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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 | 0.012 | 0.016 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.006 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.072 | 0.016 |
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".