Prevalence of diabetic foot ulcers and their associated factors in patients from public hospitals in manaus-am
Bibliographic record
Abstract
AIM: The purpose of this study was to identify and analyze the prevalence of diabetic foot ulcers (DFU) as well as associating factors in the city of Manaus, Amazonas State, Brazil. METHODS: This was an observational, epidemiological, cross-sectional study, point prevalence, with 229 adults' diabetic inpatients from seven hospitals. Written signed consent was obtained from all participants or their legal representative if they had a cognitive impairment. Sociodemographic and clinical data were collected through interviews and medical records. Each participant was examined by the research team to evaluate for foot deformity. RESULTS: Of the 229 patients diagnosed with DM, 60 presented DFU, resulting in a prevalence of 26.2 %. The logistic regression model that included all variables with a significance level of 5 % (p ≤ 0.05) shows: Patients with PAD were more likely to have DFU (OR = 2956; p = 0,01). The use of emollients (OR = 0.097; p < 0.001) and anticoagulants (OR = 0.149; p = 0.002) were related to reduced likelihood for developing DFU. CONCLUSION: This study contributes to a better understanding of DFU epidemiology in hospitalized patients, as well as the factors associated with them. The results are important for nursing in order to develop early prevention and intervention strategies.
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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 | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".