Bibliographic record
Abstract
LEARNING OBJECTIVES: After studying this article, the participant should be able to: 1. Explain the epidemiology of severe burn injury in the context of socioeconomic status, gender, age, and burn cause. 2. Describe challenges with burn depth evaluation and novel methods of adjunctive assessment. 3. Summarize the survival and functional outcomes of severe burn injury. 4. State strategies of fluid resuscitation, endpoints to guide fluid titration, and sequelae of overresuscitation. 5. Recognize preventative measures of sepsis. 6. Explain intraoperative strategies to improve patient outcomes, including hemostasis, restrictive transfusion, temperature regulation, skin substitutes, and Meek skin grafting. 7. Translate updates in the pathophysiology of hypertrophic scarring into novel methods of clinical management. 8. Discuss the potential role of free tissue transfer in primary and secondary burn reconstruction. SUMMARY: Management of burn-injured patients is a challenging and unique field for plastic surgeons. Significant advances over the past decade have occurred in resuscitation, burn wound management, sepsis, and reconstruction that have improved outcomes and quality of life after thermal injury. However, as patients with larger burns are resuscitated, an increased risk of nosocomial infections, sepsis, compartment syndromes, and venous thromboembolic phenomena have required adjustments in care to maintain quality of life after injury. This article outlines a number of recent developments in burn care that illustrate the evolution of the field to assist plastic surgeons involved in burn care.
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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.011 | 0.004 |
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".