COVID-19 and Hospital-Acquired Pressure Injuries: A Systematic Review
Bibliographic record
Abstract
OBJECTIVE: To investigate the relationship between COVID-19-related variables and hospital-acquired pressure injury (HAPI) incidence. DATA SOURCES: The authors searched four databases: Cochrane, MEDLINE, EMBASE, and CINAHL. The literature search contained key terms such as "COVID-19," "hospital-acquired pressure injuries," "pressure ulcer," "pressure injury," "decubitus ulcer," and "hospitalization." STUDY SELECTION: The systematic search of the literature identified 489 publications that matched the inclusion criteria. Articles were included in the review if they were peer-reviewed publications that reported HAPI incidence for patients who were hospitalized and COVID-19 positive. Two reviewers performed the screen simultaneously, and 19 publications were included. DATA EXTRACTION: Two reviewers followed a standardized extraction form that included study and patient characteristics, COVID-19 status, HAPI characteristics, prone positioning, length of hospitalization, and HAPI prevention and treatment strategies. DATA SYNTHESIS: The authors carried out a narrative synthesis of the extracted data because the data obtained were too heterogeneous for meta-analysis. The primary outcome was HAPI incidence. CONCLUSIONS: This review identified that HAPI incidence was high among men who were COVID-19 positive, had longer hospital stays, experienced prone positioning, and had care teams without a skin and wound care expert. Future research should use more robust methodology and focus on quantitative modeling to iteratively improve inpatient HAPI guidelines.
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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 | 0.011 | 0.054 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.007 |
| Bibliometrics | 0.008 | 0.010 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| 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".