Clinical preventive-based best practices to reduce the risk of peristomal skin complications – an international consensus report
Bibliographic record
Abstract
Evidence indicates that peristomal skin complications (PSC) are a common problem for people with an ostomy and have serious implications on their overall health and quality of life.While there is evidence and documentation on the cause and effect of PSC, there is little written on the risk factors or on how to maintain peristomal skin integrity and prevent PSC.To address this gap, a panel of ostomy experts was convened to conduct a process to reach an international consensus on PSC risk factors.A large-scale modified Delphi consensus-building process was conducted between September 2019 and October 2020.A total of 4,285 online survey responses were received from 36 countries across six continents.The result was a consensus focused on the prevention of PSC and on the individual patient risk factors healthcare providers should consider when determining the best pouching system and care plan for ostomy patients.The consensus supported the development of a PSC risk factor model.The model was subsequently ratified in October 2020.The purpose of the model is to help guide healthcare providers in assessing the risk factors for developing a PSC for each patient and ultimately guide healthcare providers to prevent skin damage, maintain healthy peristomal skin, and support the overall health, wellbeing and quality of life of ostomy patients.
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.
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 | 0.105 | 0.085 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.004 |
| Bibliometrics | 0.008 | 0.004 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.005 | 0.007 |
| Research integrity | 0.005 | 0.007 |
| Insufficient payload (model declined to judge) | 0.003 | 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".