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Record W2087477210 · doi:10.3810/hp.2014.10.1148

Management of Hospitalized Patients with Diabetic Foot Infections

2014· article· en· W2087477210 on OpenAlexaff
Mazen S. Bader, Afsáneh Alavi

Bibliographic record

VenueHospital Practice · 2014
Typearticle
Languageen
FieldMedicine
TopicDiabetic Foot Ulcer Assessment and Management
Canadian institutionsUniversity of TorontoJuravinski HospitalHamilton Health Sciences
Fundersnot available
KeywordsMedicinePsychosocialDiabetic footIntensive care medicineAmputationPatient educationDiabetes mellitusSurgeryNursing

Abstract

fetched live from OpenAlex

Diabetic foot infections (DFIs), which present with a variety of clinical manifestations, are commonly encountered by clinicians. They are associated with a high morbidity, a high amputation rate, a high mortality, and increased health care costs. An effective management of DFIs requires a multidisciplinary approach with a strong collaboration among all involved health care providers as well as patient involvement. Diagnosing DFIs appropriately requires consideration of the clinical symptoms and signs of infection in addition to supplementary laboratory testing such as inflammatory markers and imaging studies. The comprehensive patient assessment should include the predisposing risk factors for infection; the type, severity, and extent of the infection; and the assessment of neurologic and vascular status, comorbid conditions, and psychosocial factors. The comprehensive management of DFIs include not only effective antibiotic therapy but also surgical debridement, pressure offloading, wound care and moisture, maintaining good vascular perfusion, control of edema and pain, correction of metabolic abnormalities such as hyperglycemia, and addressing psychosocial and nutritional issues. Discharge planning that addresses full medical and social needs along with suitable follow-up, patient education and counseling, and clear communication with outpatient providers are critical for ensuring a safe and successful transition to outpatient management of hospitalized patients with DFIs.

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 score0.209
Threshold uncertainty score0.583

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 head0.005
GPT teacher head0.251
Teacher spread0.247 · 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

Citations4
Published2014
Admission routes1
Has abstractyes

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