Bibliographic record
Abstract
Background: Infection is the most common complication and cause of death in patients suffering burn injuries. These patients are susceptible to infection and burn wound sepsis secondary to the alterations in their physiology. Diagnosis and management of infections rely on physical examination, cultures, and the pathology of the burn wound. Method: We performed an electronic search for articles in the Google Scholar and PubMed databases using the search terms “burn sepsis,” “burn infection,” and “burn critical care.” Results: Multiple factors increase burn patients' risk of invasive infection and sepsis, including underlying factors and co-morbidities, the percent total body surface area of the burn, delays in burn wound excision, and microbial virulence/bacterial count. Organisms causing burn wound infection differ, depending on the time since injury and its location; and diagnosis is multi-factorial. The most common pathogens remain Staphylococcus and Pseudomonas spp. Conclusion: Overall, the recognition of burn sepsis is based on clinical findings. Treatment consists of a combination of local dressings, early burn excision, and systemic antimicrobial therapy. The mortality rate has decreased significantly over the past 10 years, but continued efforts at timely management and infection prevention are essential.
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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.006 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.011 | 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".