Peristomal Medical Adhesive-Related Skin Injury
Bibliographic record
Abstract
Stomal and peristomal skin complications (PSCs) are prevalent in persons living with an ostomy; more than 80% of individuals with an ostomy will experience a stomal or peristomal complication within 2 years of ostomy surgery. Peristomal skin problems are especially prevalent, and a growing body of evidence indicates that they are associated with clinically relevant impairments in physical function, multiple components of health-related quality of life, and higher costs. Several mechanisms are strongly linked to PSCs including medical adhesive-related skin injuries (MARSIs). Peristomal MARSIs are defined as erythema, epidermal stripping or skin tears, erosion, bulla, or vesicle observed after removal of an adhesive ostomy pouching system. A working group of 3 clinicians with knowledge of peristomal skin health completed a scoping review that revealed a significant paucity of evidence regarding the epidemiology and management of peristomal MARSIs. As a result, an international panel of experts in ostomy care and peristomal MARSIs was convened that used a formal process to generate consensus-based statements providing guidance concerning the assessment, prevention, and treatment of peristomal MARSIs. This article summarizes the results of the scoping review and the 21 consensus-based statements used to guide assessment, prevention, and treatment of peristomal MARSIs, along with recommendations for research priorities.
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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 | 0.006 | 0.023 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".