Bibliographic record
Abstract
SKIN TEARS are a challenging type of laceration commonly seen in older adults but frequently mismanaged. In the United States, 1.5 million skin tears occur each year in older adults who are hospitalized or living in long-term-care facilities.1 This article focuses on understanding skin tears and which patients are at risk. Part 2 will focus on managing and preventing skin tears. Shearing and friction Skin tears are traumatic wounds, most often occurring on the extremities, in which shearing or friction causes the epidermis to separate from the dermis, or the epidermis and the dermis to separate from underlying structures.2 Older adults are at higher risk for skin tears for various age-related reasons (more on these shortly).3 Compared to more extensive and costly pressure ulcers, skin tears are often considered minor, inconsequential wounds. In reality, these wounds are painful and can lead to complications such as infection if not treated appropriately.4 Nearly 80% of skin tears occur on the arms and hands, but they can occur anywhere on the body; on the buttocks and back, they can be mistaken for Stage II pressure ulcers.1 The Payne-Martin Classification for Skin Tears is widely used in research and in the literature to define and classify these wounds (see Classifying skin tears).3,5 Age-related changes Skin changes associated with aging increase the risk of skin tears and interfere with normal wound healing.1,2Intrinsic risk factors include dermal and subcutaneous tissue loss, epidermal thinning, and serum composition changes, which mean that older adults have decreased skin surface moisture, reduced skin elasticity, and reduced skin tensile strength.1 The risk of skin tears is further increased by dehydration, poor nutrition, cognitive impairment, altered mobility, and decreased sensation.3,6Figure: Classifying skin tears7Extrinsic risk factors include the risk of mechanical trauma and the need for assistance with bathing, dressing, toileting, and transferring. Patients dependent on others for total care are at the greatest risk for skin tears. Because soaps reduce the skin's natural lubrication, frequent bathing coupled with the natural decrease in lubrication associated with aging can increase an older adult's risk for skin tears.4,5 In a future article, we'll explore how you can manage and prevent skin tears.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.050 | 0.022 |
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".