Bibliographic record
Abstract
LEARNING OBJECTIVES: After studying this article and accompanying Supplemental Digital Content, the participant should be able to: 1. Explain current burn-prevention strategies and criteria for referral to a burn center. 2. Summarize the current advances made in the critical care of acute burn patients. 3. Outline the recent developments in burn depth assessment and burn wound dressing technology. 4. Describe the common psychosocial aspects of postburn rehabilitation. SUMMARY: Burn patients require interdisciplinary care in which the plastic surgeon plays a prominent role. Appropriate referral, assessment, treatment, and posttreatment supports are essential to achieving favorable outcomes following burn injury. The authors reviewed the current literature on epidemiology, prevention, referral criteria, critical care, wound assessment, wound dressings, and psychosocial aspects of burn injury. Recent advances in burn care are highlighted and have been made possible through ongoing collaborative epidemiologic, clinical, and basic biomedical research. A systematic interdisciplinary approach to the evaluation and treatment of acute burn injuries is pivotal to providing patients with the greatest chance of functional recovery. Plastic surgeons treating burn patients must remain current in a wide variety of areas, ranging from critical care to psychosocial rehabilitation.
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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.148 | 0.046 |
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".