Skin tear prevalence and incidence in the long-term care population: a prospective study
Bibliographic record
Abstract
OBJECTIVE: The World Health Organization estimates that between 2015 and 2050 the proportion of the world's population over 60 years old will nearly double from 12% to 22%. An often overlooked byproduct of ageing is the skin changes associated with it, which heighten the risk of developing skin tears. Despite this presumed increased risk, the true impact of skin tears across age groups and care settings is poorly understood. The purpose of the present study was to establish the prevalence and incidence of skin tears in the Ontario long-term care population. METHOD: A prospective study design was used to explore the prevalence and incidence of skin tears. Individuals from four long-term care facilities in Ontario were followed over four weeks. The participants were examined for skin tears at the beginning of the study and at week four to determine whether skin tears had occurred and to record the skin tear type and location. RESULTS: A total of 380 individuals, aged 65 years and over, took part. The study found a skin tear prevalence of 20.8% and an incidence of 18.9% within four weeks. These results provide much needed data on the burden of skin tears in the long-term care population. Conclusion: The present study is an important first step towards developing a prevention programme targeting individuals at risk for skin tears in long-term care.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".