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Record W2472040718

Awareness of foot care among patients with diabetes attending a tertiary care hospital.

2016· article· en· W2472040718 on OpenAlexaboutno aff
Ashish Datt Upadhyay

Bibliographic record

VenuePubMed · 2016
Typearticle
Languageen
FieldMedicine
TopicDiabetic Foot Ulcer Assessment and Management
Canadian institutionsnot available
Fundersnot available
KeywordsFoot (prosody)MedicineDiabetes mellitusFoot careTertiary careHealth careFamily medicineDiabetic footQuarter (Canadian coin)
DOInot available

Abstract

fetched live from OpenAlex

BACKGROUND: The prevalence of diabetes in India is rising. It is also occurring at a younger age. Ulcers on the foot in patients with diabetes are a common cause of amputations and are largely preventable. We assessed the awareness of foot care among patients with diabetes attending a tertiary care hospital in northern India and whether this varied with the level of healthcare availed, i.e. primary, secondary or tertiary. METHODS: A scored questionnaire was designed based on foot care practices advised by the American Diabetes Association as part of the national diabetes education programme. It was administered to 400 patients and a total foot care score was calculated and correlated with various variables. RESULTS: Only 50 of 400 patients (12.5%) had received previous foot care advice from healthcare professionals, and 193 (48.2%), 28 (7%) and 179 (44.8 %) patients were being taken care of by primary, secondary and tertiary healthcare systems, respectively. Almost one-quarter of patients were uneducated. The mean foot care score in all three groups was 5 of a maximum of 14, which was poor. CONCLUSION: The awareness of foot care among people with diabetes is low among those attending all levels of healthcare: primary, secondary and tertiary. It is necessary to educate people about foot care, lack of which will lead to a huge financial and health burden due to preventable complications of diabetes.

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 score0.135
Threshold uncertainty score0.398

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 head0.006
GPT teacher head0.206
Teacher spread0.200 · 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

Citations24
Published2016
Admission routes1
Has abstractyes

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