Burn injury: review of pathophysiology and therapeutic modalities in major burns.
Bibliographic record
Abstract
Despite a considerable decrease in their incidence worldwide, burn injuries remain one of the commonest forms of trauma and account for a weighty proportion of trauma cases in health-care emergencies around the globe. Although the latest data reveal a substantial decline in burn-related mortality and hospital admissions in the US over the past three decades, severe thermal injuries continue to trigger devastating morbidity and significant mortality while their management remains a dynamic challenge for the entire medical and paramedical community. Concrete evidence continues to be established regarding burn-associated pathophysiologic responses, and their destructive sequelae and deleterious effects in survivors at cellular, systemic as well as socio-economic level. Better understanding of these responses have contributed to advances in therapeutic strategies, improved long-term outcomes and catalyzed the reintegration of victims back into society. This paper describes the current understanding of the pathophysiology of a burn injury and characterizes both local and systemic pathophysiologic responses in terms of metabolic, hemodynamics, cardiac, renal, hepatic, gastro-intestinal, immunologic, endocrine as well as male reproductive systems in an attempt to understand the corresponding treatment modalities for this unique patient population.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".