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Protocol for Psychopharmacologic Management of Behavioral Health Comorbidity in Adult Patients with Diabetes and Soft Tissue Infections in a Tertiary Care Hospital Setting

2016· article· en· W2534613172 on OpenAlexaff
Aaron Pinkhasov, Deepan Singh, Benjamin Kashan, Julie DiGregorio, Theresa Criscitelli, Scott Gorenstein, Harold Brem

Bibliographic record

VenueAdvances in Skin & Wound Care · 2016
Typearticle
Languageen
FieldMedicine
TopicWound Healing and Treatments
Canadian institutionsNovelis (Canada)
FundersNational Institute of Diabetes and Digestive and Kidney Diseases
KeywordsMedicineDiabetes mellitusAnxietyComorbidityDepression (economics)GlycemicPsychiatryPopulation

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.114
Threshold uncertainty score0.422

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.009
GPT teacher head0.368
Teacher spread0.359 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2016
Admission routes1
Has abstractyes

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