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 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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".