Surgical Options for Advanced Pressure Injuries
Bibliographic record
Abstract
Advanced pressure injuries (PI), the debilitating consequence of prolonged pressure on the skin, are devastating wounds that are commonly refractory to treatment and are a source of significant patient morbidity and monetary strain on the healthcare system. This article outlines the fundamental principles for the conservative management of PIs, emphasizing risk factor mitigation, patient optimization, and preventive strategies. In addition, the article explores different surgical interventions for cases where PIs have progressed beyond conservative treatment options. GENERAL PURPOSE: To demonstrate knowledge of the evidence-based fundamental measures and surgical options to manage advanced pressure injuries (PIs). JOURNAL/aswca/04.03/00129334-202508000-00001/figure1/v/2025-07-18T083052Z/r/image-jpeg LEARNING OBJECTIVES/OUTCOMES: After participating in this educational activity, the participant will 1. Identify factors that place a patient at risk for PI. 2. Differentiate between the various stages and types of PI. 3. Apply evidence-based management strategies to treat patients with PI.
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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.078 | 0.010 |
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".