Status and Challenges of Predicting and Diagnosing Sepsis in Burn Patients
Bibliographic record
Abstract
Burns are a common form of trauma that account for more than 300,000 deaths each year worldwide. Survival rates have improved over the past decades because of improvements in nutritional and fluid support, burn wound care, and infection control practices. Death, however, remains unacceptably high. The primary cause of death has changed over the last decades from anoxic causes to now predominantly infections and sepsis. Sepsis and septic complications are not only major contributors to poor outcomes, but they further result in longer hospital stay and higher healthcare costs. Despite the importance of infections and sepsis, the diagnosis and prediction remain a major challenge. To date, no clear diagnostic criteria or predictive formula exist that can predict reliably the occurrence of sepsis and infections. This review will highlight and discuss current definitions and criteria for diagnosis as well as predictive biomarkers of sepsis in patients with burns. It will also present the diagnostic tools employed, such as procalcitonin, C-reactive protein, and cytokines. We will discuss the benefits and shortcomings of different treatment modalities in the context of sepsis prevention. Last, we identify new therapeutic strategies for sepsis prediction and present future considerations to prevent sepsis in patients with burns. Minimizing and preventing septic complications through early detection would significantly benefit patients and necessitate continued research to unravel new biomarkers and mechanisms. Subsequent studies need to take a fresh perspective and consider the implementation of patient-centered therapeutic strategies.
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 | 0.003 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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".