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Record W4313414808 · doi:10.3390/medicina59010064

Diabetic Foot Care: A Screening on Primary Care Providers’ Attitude and Practice in Riyadh, Saudi Arabia

2022· article· en· W4313414808 on OpenAlexaff
Sultan Alsheikh, Hesham AlGhofili, Reema Abdulaziz Alageel, Omar A. Ababtain, Ghadah A. Alarify, Nasser Alwehaibi, Abdulmajeed Altoijry

Bibliographic record

VenueMedicina · 2022
Typearticle
Languageen
FieldMedicine
TopicDiabetic Foot Ulcer Assessment and Management
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsMedicineFamily medicinePrimary careFoot (prosody)Diabetic footObservational studyDiabetes mellitusMultidisciplinary approachInternal medicine

Abstract

fetched live from OpenAlex

Background and Objectives: Diabetic foot (DF) disease is one of the myriad complications of diabetes. Positive outcomes are expected through a multidisciplinary approach as provided by primary care providers (PCPs). This study aimed to assess the knowledge of DF and attitude of physicians in primary healthcare settings toward DF diagnosis and prevention in Saudi Arabia. Materials and Methods: This observational cross-sectional study used a self-administered questionnaire that was completed by family medicine consultants, residents, and general practitioners working in primary care settings in Riyadh. Results: Of the 152 physicians who completed the survey, (43.4%) completed more than 10 h of diabetes continuing medical education (CME) over the past three years. Most (96.1%) PCPs educate patients about foot self-inspection, and only (64.5%) perform foot inspection at every visit in high-risk diabetic foot patients. PCP knowledge about diagnosing and managing diabetic foot infection was suboptimal. Only 53.9% of participants reported performing a probe-to-bone in DF patients with open wounds. Conclusions: We identified knowledge and action gaps among PCPs. Physicians had acceptable knowledge about preventive measures. However, deficits were found regarding diagnosing and management of DF infections. We recommend addressing these knowledge gaps by incorporating DF lectures and workshops within family medicine conferences and residency programs.

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)0.001
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: Other design · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.741
Threshold uncertainty score0.836

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 integrity0.0000.001
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 head0.017
GPT teacher head0.289
Teacher spread0.271 · 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 designOther design
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

Citations12
Published2022
Admission routes1
Has abstractyes

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