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Demystifying skin tears, part 1

2010· article· en· W2127017172 on OpenAlex
Kimberly LeBlanc, Dawn Christensen

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueNursing · 2010
Typearticle
Languageen
FieldHealth Professions
TopicPressure Ulcer Prevention and Management
Canadian institutionsProfessional Engineers Ontario
Fundersnot available
KeywordsTearsMedicineDermisButtocksDermatologySurgeryPathology

Abstract

fetched live from OpenAlex

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.

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.

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.845
Threshold uncertainty score0.998

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0030.001

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.

Opus teacher head0.059
GPT teacher head0.457
Teacher spread0.398 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it