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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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; 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".