Bibliographic record
Abstract
In Brief PURPOSE: To enhance the learner's competence with knowledge of forces that affect skin breakdown and available bed surface support selections to help reduce the incidence of pressure ulcers (PrUs). TARGET AUDIENCE: This continuing education activity is intended for physicians and nurses with an interest in skin and wound care. OBJECTIVES: After participating in this educational activity, the participant should be better able to: Compare and contrast use of active and reactive support surfaces. Relate pressure, friction, and shear forces to use of active and reactive support surfaces in prevention of PrUs. Apply the principles learned from this activity in determining appropriate support surface selections for patients with various clinical scenarios. The prevention and management of pressure ulcers, including support surface selection, are a primary focus of healthcare providers. This article discusses the forces contributing to pressure ulcer formation and explores choosing therapeutic support surface features based on the patient's clinical needs and on using the evidence-informed support surface algorithm and decision trees. This continuing education activity explores the forces contributing to pressure ulcer formation and explores choosing therapeutic support surface features based on the patient's clinical needs and on using the evidence-informed support surface algorithm and decision trees.
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 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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 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".