Glycemic Control Rate in Type 2 Diabetes Mellitus Patients at a Public Referral Hospital in Rio de Janeiro, Brazil: Demographic and Clinical Factors
Bibliographic record
Abstract
Background: The aim of the study was to determine the rate of satisfactory glycemic control among patients with type 2 diabetes (T2D) followed up at a tertiary referral hospital in Brazil. Methods: A retrospective and observational study was conducted between September 2014 and September 2015, by collecting data from medical records. Results: Data were obtained from 1,001 patients. The majority of patients were women (68%), with a median age of 61 years old (21 - 95). The median duration of disease was 10 years (1 - 58). Satisfactory glycemic control rate was found in 51% of patients. The strongest factors related to good control were: younger age (P < 0.001); absence of a T2D family history (P = 0.04), obesity (P < 0.001), overweight (P < 0.001), and absence of current alcohol consumption (P = 0.006); presence of fewer comorbidities (P = 0.01), chronic kidney disease (P = 0.004), and the treatment using only oral antidiabetic drugs (OADs) (P < 0.001). Conclusions: The majority of patients obtained an adequate glycemic control rate, particularly among those using only OAD. Younger age, a negative family history of T2D, normal body mass index, absence of current alcohol consumption, presence of fewer comorbidities, and chronic kidney disease were associated with better glycemic control. J Endocrinol Metab. 2017;7(2):61-67 doi: https://doi.org/10.14740/jem390w
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".