Acute wound management: revisiting the approach to assessment, irrigation, and closure considerations
Bibliographic record
Abstract
BACKGROUND: As millions of emergency department (ED) visits each year include wound care, emergency care providers must remain experts in acute wound management. The variety of acute wounds presenting to the ED challenge the physician to select the most appropriate management to facilitate healing. A complete wound history along with anatomic and specific medical considerations for each patient provides the basis of decision making for wound management. It is essential to apply an evidence-based approach and consider each wound individually in order to create the optimal conditions for wound healing. AIMS: A comprehensive evidence-based approach to acute wound management is an essential skill set for any emergency physician or acute care practitioner. This review provides an overview of current evidence and addresses frequent pitfalls. METHODS: A systematic review of the literature for acute wound management was performed. RESULTS: A structured MEDLINE search was performed regarding acute wound management including established wound care guidelines. The data obtained provided the framework for evidence-based recommendations and current best practices for wound care. CONCLUSION: Acute wound management varies based on the wound location and characteristics. No single approach can be applied to all wounds; however, a systematic approach to acute wound care integrated with current best practices provides the framework for exceptional wound management.
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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 | 0.014 | 0.037 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.009 | 0.006 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.005 | 0.009 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.004 | 0.008 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".