A descriptive cross‐sectional international study to explore current practices in the assessment, prevention and treatment of skin tears
Bibliographic record
Abstract
This study presents the results of a descriptive, cross-sectional, online international survey in order to explore current practices in the assessment, prediction, prevention and treatment of skin tears (STs). A total of 1127 health care providers (HCP) from 16 countries completed the survey. The majority of the respondents (69·6%, n = 695) reported problems with the current methods for the assessment and documentation of STs with an overwhelming majority (89·5%, n = 891) favouring the development of a simplified method of assessment. Respondents ranked equipment injury during patient transfer and falls as the main causes of STs. The majority of the samples indicated that they used non-adhesive dressings (35·89%, n = 322) to treat a ST, with the use of protective clothing being the most common method of prevention. The results of this study led to the establishment of a consensus document, classification system and a tool kit for use by practitioners. The authors believe that this survey was an important first step in raising the global awareness of STs and to stimulate discussion and research of these complex acute wounds.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".