Skin and Wound Map From 23,453 Nursing Home Resident Records: Relative Prevalence Study
Bibliographic record
Abstract
Background: The overall distribution of all skin and wound problems experienced by residents in skilled nursing facilities, with respect to the location on the body, is poorly understood. Previous studies focused largely on one disease type, rather than all possible skin lesions. Hence, the relative distribution of skin and wound problems as mapped on the body has not previously been reported. In addition, existing data come mainly from clinical studies and voluntarily reported statistics; unbiased real-world evidence is lacking. Objective: The aim of this study was to understand the type and location of skin and wound lesions found in skilled nursing facilities and to map these on the body. Methods: Data from 23,453 wounds were used to generate heat maps to identify the most common areas of skin and wound lesions, as well as the most common wound types at different body locations. Results: The most common wound types were abrasion (8792/23,453, 37.49%), pressure ulcers (4089/23,453, 17.43%), surgical wounds (3107/23,453, 13.25%), skin tears (2206/23,453, 9.41%), and moisture-associated skin damage (959/23,453, 4.09%). The most common skin and wound locations were the coccyx (962/23,453, 4.10%), right (853/23,453, 3.64%) and left (841/23,453, 3.59%) forearms, and sacrum (818/23,453, 3.49%). Conclusions: Here, we present the body location hot spots of skin and wound lesions experienced by residents of skilled nursing facilities. In addition, the relative prevalence of these conditions is presented. We believe that identifying areas on the body prone to preventable wounds can help direct actions by care workers and improve the quality of care for skilled nursing residents. This study represents an example of how analysis of specialized electronic medical records can be used to generate insights to educate and inform facility managers where to focus their efforts to prevent these injuries from occurring, not only from retrospective database analysis but also in near real time.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 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 | 0.001 |
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; both teacher heads agree on what is shown here.
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".