Frailty in Diabetic Subjects during COVID-19 and Its Association with HbA1c, Mean Platelet Volume and Monocyte/Lymphocyte Ratio
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".