Prevalence of Pressure Ulcers in the ICUs of Iranian Hospitals: A Systematic Review and Meta-analysis
Bibliographic record
Abstract
Background: Patient safety is a fundamental right and a core priority in healthcare systems, aiming to prevent harm during diagnosis and treatment. Pressure ulcers are a common problem in intensive care units (ICUs), causing significant physical, psychological, social, and financial burdens. Although several studies have been conducted in Iran, data remain fragmented and region-specific, preventing a comprehensive national estimate.Aim: This systematic review was conducted with aim to determine the prevalence of pressure ulcers in the ICUs of Iranian hospitals.Method: This study was conducted as a systematic review and meta-analysis following Gough’s nine-stage framework. Searches were performed in the international and national databases up to July 22, 2025, resulting in 16 eligible studies. Study quality was assessed using a modified Newcastle-Ottawa Scale. Statistical analyses were performed with Comprehensive Meta-Analysis software using a random-effects model.Results: After excluding studies with potential bias, the pooled prevalence of pressure ulcers in Iranian ICU patients was estimated at 6.6% (5.2%-8.4%; 95% CI). The highest prevalence was reported in the eastern provinces. Meta-regression showed a significant positive association between patient age and pressure ulcer prevalence, and an inverse association with sample size (p<0.05).Implications for Practice: Pressure ulcers pose serious physical, psychological, social, and financial challenges for ICU patients. Healthcare facilities must prioritize the implementation of effective prevention protocols tailored to at-risk patients. Regular, ongoing training and workshops for healthcare staff are essential to enhance knowledge and skills in pressure ulcer prevention. Additionally, educating patients and their families about risk factors and preventive measures is vital to reduce the incidence of pressure ulcers and improve overall patient care outcome
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 | 0.013 | 0.031 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.017 | 0.036 |
| Bibliometrics | 0.010 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| 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".