Moisture-associated skin damage and its management in neonatal and infant populations: a retrospective study in Italy
Bibliographic record
Abstract
OBJECTIVE: Moisture-associated skin damage (MASD) is a significant concern in the paediatric population, particularly among neonates, who exhibit the highest incidence due to their highly sensitive and fragile skin. The aim of the study was to identify the most effective treatment. METHOD: A retrospective observational study was conducted at Meyer Children's Hospital IRCCS, Florence, Italy. Data from infants admitted to the neonatal intensive care unit of the hospital were collected from electronic health records. Demographic, clinical and nursing data were analysed to identify the key clinical features of MASD lesions, including their severity, healing time, and the relationship between MASD lesions and treatment approaches. RESULTS: The cohort comprised 102 infants (mean age 2.92 months). Incontinence-associated dermatitis (IAD) accounted for the majority (78.4%) of MASD lesions. Among patients with IAD, most exhibited persistent redness (26.3%) or skin loss (47.5%) without clinical signs of infection. In contrast, peristomal MASD cases constituted 21.6% of the total, with the majority classified according to the Study on Peristomal Skin Alterations (SACS) 2.0 as L2 erosive lesions (63.6%), predominantly occurring in quadrants 2, 3 or 5. A novel treatment (Vulnamin; Professional Dietetics S.p.A., Italy) was used to manage MASD lesions in 64.7% of patients. This innovative treatment significantly (p<0.001) reduced healing time by an average of 2.4 days and decreased the odds of relapse by 5.3 times compared with traditional treatments. CONCLUSION: The findings of this study showed that use of effective MASD therapies can reduce healing time and relapse rate, leading to decreased nursing hours and associated costs.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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 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".