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A summary of evidence for diabetic foot assessment

2019· article· en· W3029724876 on OpenAlexaboutno aff
Peiying Zhang, Wei Wang, Gaoqiang Li, Huijuan Li, Qian Lü, Jun Deng, Yanming Ding

Bibliographic record

VenueZhonghua xiandai huli zazhi · 2019
Typearticle
Languageen
FieldMedicine
TopicDiabetic Foot Ulcer Assessment and Management
Canadian institutionsnot available
Fundersnot available
KeywordsExcellenceMedicineDiabetic footGuidelineCINAHLNiceEvidence-based medicineFoot (prosody)MEDLINEEvidence-based practiceFamily medicineDiabetes mellitusAlternative medicineNursingPathologyPolitical science

Abstract

fetched live from OpenAlex

Objective To search, appraise and summarize the best evidence of the diabetic foot assessment and provide a reference for the standardization of the clinical diabetic foot evaluation in China. Methods On the computer, the following websites as well as databases were searched: National Guideline Clearinghouse (NGC) of the USA, Registered Nurses' Association of Ontario (RNAO) , National Institute for Health and Care Excellence (NICE) of UK, the Scottish Intercollegiate Guidelines Network (SIGN) , New England Guidelines Group (NEGG) , International Guidelines Network (IGN) , American Center for Disease Control and Prevention (ACDC) , World Council of Enterostomal therapists (WCET) , Wound Ostomy Continence Nurses Society (WOCN) , the International Working Group on Diabetic Foot (IWGDF) , American Society for Wound Healing, American Diabetes Association (ADA) , American Society for Vascular Surgery (ASVS) , Society of Endocrinology of Chinese Medical Association, Clinical evidence, PubMed, ProQuest, Web of Science, Clinical Key, CINAHL, Best Practice, CNKI, VIP, Wanfang Data, Medlive from January of 2010 to July of 2018 about all evidence-based clinical practice guidelines and systematic reviews on assessment of diabetic foot. Two researchers evaluated the quality of the articles and performed materials extraction, and extracted the evidence from the qualified articles. Results A total of 7 articles were included among which 6 were guidelines and 1 was systemic review. After analyzing them, 9 categories and 32 items of the best evidence contents about diabetic foot assessment were concluded. Conclusions This study summarizes the best evidence for assessment of diabetic foot and provide evidence basis for medical institutions to improve clinical practice of diabetic foot assessment. When applying the evidence into clinical practice, it is necessary to evaluate specific situations, consider patients' values and willingness and select targeted evidence accordingly. Key words: Diabetic foot; Foot ulcer; Assessment; Best evidence; Evidence summary

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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.160
Threshold uncertainty score0.783

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
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)0.0010.000

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 head0.070
GPT teacher head0.366
Teacher spread0.296 · 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
Published2019
Admission routes1
Has abstractyes

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