Peristomal Skin Complications Are Common, Expensive, and Difficult to Manage: A Population Based Cost Modeling Study
Bibliographic record
Abstract
BACKGROUND: Peristomal skin complications (PSCs) are the most common post-operative complications following creation of a stoma. Living with a stoma is a challenge, not only for the patient and their carers, but also for society as a whole. Due to methodological problems of PSC assessment, the associated health-economic burden of medium to longterm complications has been poorly described. AIM: The aim of the present study was to create a model to estimate treatment costs of PSCs using the standardized assessment Ostomy Skin Tool as a reference. The resultant model was applied to a real-life global data set of stoma patients (n = 3017) to determine the prevalence and financial burden of PSCs. METHODS: Eleven experienced stoma care nurses were interviewed to get a global understanding of a treatment algorithm that formed the basis of the cost analysis. The estimated costs were based on a seven week treatment period. PSC costs were estimated for five underlying diagnostic categories and three levels of severity. The estimated treatment costs of severe cases of PSCs were increased 2-5 fold for the different diagnostic categories of PSCs compared with mild cases. French unit costs were applied to the global data set. RESULTS: The estimated total average cost for a seven week treatment period (including appliances and accessories) was 263€ for those with PSCs (n = 1742) compared to 215€ for those without PSCs (n = 1172). A co-variance analysis showed that leakage level had a significant impact on PSC cost from 'rarely/never' to 'always/often' p<0.00001 and from 'rarely/never' to 'sometimes' p = 0.0115. CONCLUSION: PSCs are common and troublesome and the consequences are substantial, both for the patient and from a health economic viewpoint. PSCs should be diagnosed and treated at an early stage to prevent long term, debilitating and expensive complications.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".