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Common foot examination features of 247 Iranian patients with diabetes

2009· article· en· W2103049630 on OpenAlexaff
Afsáneh Alavi, Mojgan Sanjari, Ali Akbar Haghdoost, R. Gary Sibbald

Bibliographic record

VenueInternational Wound Journal · 2009
Typearticle
Languageen
FieldMedicine
TopicDiabetic Foot Ulcer Assessment and Management
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsMedicineFoot (prosody)Diabetes mellitusDiabetic footDermatologyEndocrinology

Abstract

fetched live from OpenAlex

BACKGROUND: Iran is a Middle Eastern country with a 70 million population. There are 3 million Iranians with diabetes mellitus (DM) and there is a high incidence of non traumatic amputation in this population. Amputation is often preceded by foot deformity or ulceration. We evaluated the routine foot examination of persons with diabetes (PWD) attending an outpatient Diabetic Clinic to identify the clinical characteristics that might be early warning signs of individuals at a high risk of developing a foot ulcer or having a subsequent non traumatic amputation. METHODS: A prospective, descriptive, clinic-based study was conducted on 247 patients with diabetes mellitus in 2005. The objectives of the study were to define the abnormal features of the foot examination in PWD which could be risk factors for ulceration or amputation. RESULTS: The mean age of patients with diabetes was 52 +/- 12. The prevalence of callus in the enrolled patients was 12% and heel fissures were noted in 50%. There was a significant relationship between callus formation and the absence of tibialis posterior pulse (odds ratio 5), the presence of the hammer toe deformity (odds ratio 4), and foot ulceration (odds ratio 3). The prevalence of foot ulcers in PWD was 4%. CONCLUSION: A diabetic screening program identifying callus formation, absent pulses, and hammer toe are important early signs of individuals at an increased risk for foot ulcers. This program will facilitate early treatment to decrease the loss of limbs.

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 score0.290
Threshold uncertainty score0.306

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 head0.008
GPT teacher head0.263
Teacher spread0.255 · 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

Citations25
Published2009
Admission routes1
Has abstractyes

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