Early Recognition and Emergency Treatment of Sepsis and Septic Shock in Children
Bibliographic record
Abstract
Early diagnosis and treatment of sepsis and septic shock in children results in improved outcomes. However, diagnosis is hampered by lack of specific diagnostic tests and relies on the recognition of the alterations of vital signs and protean systemic manifestations associated with infections, signs that mimic many critical illnesses. As a result, the early diagnosis of sepsis is usually presumptive and is based on the suspicion or presence of an infection in combination with the systemic changes. Suspicion should be heightened in vulnerable risk groups such as those with immune compromise due to underlying disease or medication use. Thus, on many occasions, treatment of sepsis is initiated on clinical suspicion pending the outcomes of ongoing evaluations and laboratory findings.What is of relevance to the emergency clinicians is the initial recognition, resuscitation, and treatment in the first few hours of presentation. To best accomplish these tasks, contemporary guidelines suggest that the use of a "recognition bundle" containing a trigger tool for rapid identification, a "resuscitation and stabilization bundle" to enable adherence to best practice, and a "performance bundle" to identify and overcome barriers to best practice be used.Although there are no universally acceptable tools to accomplish these tasks, the various iterations used in quality improvement initiatives have consistently demonstrated better care processes and outcomes. In this article, we outline the contemporary approach to sepsis in the first hours after presentation.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".