High prevalence of skin and wound care of hospitalized elderly in Brazil: a prospective observational study
Bibliographic record
Abstract
BACKGROUND: Skin changes caused by aging increase the risk of skin damages, such as pressure ulcers, during hospitalization of elderly patients. There is few information about the cost of wound treatment in Brazil. Conversely, skin and wound problems are highly reported among hospitalized elderly patients and caregivers. The purpose is to analyze the socio-demographic and clinical profile associated with skin and wound care in hospitalized elderly. METHODS: This is a prospective observational study. The sample consisted of 75 patients, aged 60 years or more, randomly selected in three hospitals in Rio de Janeiro, Brazil. Data extraction from nursing records of the sample, using cross mapping with Nursing Interventions Classification. Data Synthesis supported by SAS 6.11 (SAS Institute, Inc. Cary North Carolina) in association with SPSS version 14.0 and statistics analysis. RESULTS: The findings were: age standard deviation 7.8, with minimum as 60, and maximum as 91 years old. Prevalence of women and married seniors. High prevalence of long-term hospitalization. There were 21 Nursing Interventions in the nursing records and seventeen of them related to skin and wound care. They were described in 57 nursing activities, present during 376 evaluations and repeated 1756 times. A significant difference was obtained between age and the presence of the nursing interventions "Positioning" (p-0.004), Eye Care/Hygiene (p- < 0.0001) and Oral Health Maintenance (p-0.0003). CONCLUSION: The skin care to prevention and treatment of skin damages represented the major demand of nursing interventions in different clinical conditions of hospitalized elderly.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| 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".