A descriptive cross‐sectional international study to explore current practices in the assessment, prevention and treatment of skin tears
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.
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.
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 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 | 0.003 | 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 | 0.000 | 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 | 0.000 |
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 it