Maintaining healthy skin around an ostomy: peristomal skin disorders and self-assessment
Bibliographic record
Abstract
Problems affecting the peristomal skin are common in people with an ostomy and are an important area of stoma care nurse intervention. The DialogueStudy documented the experiences of more than 3000 people with an ostomy, with a focus on quality of life (QoL) and peristomal skin condition. These factors were assessed using the Ostomy Skin Tool. At visit 1, 60% of participants had a skin disorder, with irritant contact dermatitis (48%) and mechanical trauma (21%) being the most common causes cited. Only 53% of participants with a skin disorder were aware of it. The mean DET score had improved (decreased) from 2.5 (±2.8) at visit 1 to 1.6 (±2.1) at visit 2, 6–8 weeks later (P<0.0001). The factors affecting peristomal skin included age, time since surgery, ostomy type, choice of appliance and leakage. The results demonstrate that the combination of evidence-based nursing practices and the use of a double-layer adhesive, SenSura (Coloplast A/S) improved peristomal skin condition in people with an ostomy.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 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 | 0.000 |
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 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".