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Record W4224306919 · doi:10.5603/dk.a2022.0015

Frailty in Diabetic Subjects during COVID-19 and Its Association with HbA1c, Mean Platelet Volume and Monocyte/Lymphocyte Ratio

2022· article· en· W4224306919 on OpenAlexaboutno aff
Burçin Meryem Atak Tel, Satılmış Bilgin, Özge Kurtkulağı, Gizem Kahveci, Tuba Taslamacıoğlu Duman, Tugrul Sagdic, Gülali Aktaş

Bibliographic record

VenueClinical Diabetology · 2022
Typearticle
Languageen
FieldMedicine
TopicInflammatory Biomarkers in Disease Prognosis
Canadian institutionsnot available
Fundersnot available
KeywordsMedicineDiabetes mellitusInternal medicineCreatinineMean platelet volumeType 2 Diabetes MellitusPopulationLymphocyteType 2 diabetesImmunologyPlateletEndocrinology

Abstract

fetched live from OpenAlex

Background: Frailty is associated with increased risk of hospitalization in diabetic patients. Both SARS-CoV-2 pandemic and type 2 diabetes mellitus contribute to the frailty. In this study we aimed to observe clinical and laboratory indices of the diabetic subjects during COVID-19 pandemic who were either frail or not according to Edmonton frail score. Material and methods: During the pandemic era, 100 consecutive patients with type 2 diabetes mellitus divided into two groups either as frail or non-frail according to the Edmonton Frail Scale scores. Laboratory and clinical features of the frail and non-frail subjects were compared. Results: Frail patients were older than the non-frail diabetics. Blood urea, serum creatinine, eGFR, plasma albumin, total cholesterol, triglyceride, HbA1c, mean platelet volume (MPV), and monocyte lymphocyte ratio (MLR) levels of the frail and non-frail groups were significantly different. Moreover, Edmonton frail score was significantly and positively correlated with blood urea, serum creatinine, MLR, MPV, HbA1c and inversely correlated with eGFR and plasma albumin levels. Conclusions: We think that HbA1c, MPV and MLR could be surrogate markers of frailty in diabetic elderly during COVID-19 outbreak. Strategies to keep them in normal range do not only improve diabetes control but also reduce the risk of frailty in this population.

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)0.002
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 score0.012
Threshold uncertainty score0.864

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.002
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 integrity0.0000.001
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 head0.037
GPT teacher head0.326
Teacher spread0.289 · 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

Citations32
Published2022
Admission routes1
Has abstractyes

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