The Incidence and Risk Factors for Medical Adhesive‐Related Skin Injury in Cancer Patients in China: A Systematic Review and Meta‐Analysis
Bibliographic record
Abstract
ABSTRACT To explore the incidence and risk factors for medical adhesive‐related skin injury (MARSI) in cancer patients in China, and to provide a basic framework for approaches designed to reduce the occurrence of MARSI and improve the management of its risk factors. PubMed, Web of Science, The Cochrane Library, EmBase, CNKI, VIP, Wanfang, and CBM were searched from database inception to October 2024. The Agency for Healthcare Research and Quality and the Newcastle–Ottawa Scale were used to assess the quality of the included studies, and a random‐effects model with Stata 15.0 software was utilized for calculating the pooled incidence and risk factor for MARSI in cancer patients. A total of 18 studies were included, with 11,393 patients. Meta‐analysis showed that the pooled incidence of MARSI in Chinese cancer patients was 24%. In subgroup analyses, dermatitis demonstrated the highest incidence rate (10%). MARSI history, allergy history, dressing type (3M), puncture site (upper arm), gender (female), BMI > 25 kg/m 2 , moist skin, and age (≥ 50 years) were risk factors for MARSI in cancer patients. The incidence of MARSI in Chinese cancer patients is high, and MARSI history, allergy history, dressing type (3M), puncture site, gender (female), BMI > 25 kg/m 2 , moist skin, and age (≥ 50 years) were risk factors for the occurrence of MARSI in Chinese cancer patients, suggesting that early identification and protection of high‐risk patients, and timely targeted preventive measures are important to reduce the incidence of MARSI in cancer patients.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.010 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.006 | 0.001 |
| Bibliometrics | 0.001 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".