Operationalizing Appropriate Sepsis Definitions in Children Worldwide: Considerations for the Pediatric Sepsis Definition Taskforce
Bibliographic record
Abstract
Sepsis is a leading cause of global mortality in children, yet definitions for pediatric sepsis are outdated and lack global applicability and validity. In adults, the Sepsis-3 Definition Taskforce queried databases from high-income countries to develop and validate the criteria. The merit of this definition has been widely acknowledged; however, important considerations about less-resourced and more diverse settings pose challenges to its use globally. To improve applicability and relevance globally, the Pediatric Sepsis Definition Taskforce sought to develop a conceptual framework and rationale of the critical aspects and context-specific factors that must be considered for the optimal operationalization of future pediatric sepsis definitions. It is important to address challenges in developing a set of pediatric sepsis criteria which capture manifestations of illnesses with vastly different etiologies and underlying mechanisms. Ideal criteria need to be unambiguous, and capable of adapting to the different contexts in which children with suspected infections are present around the globe. Additionally, criteria need to facilitate early recognition and timely escalation of treatment to prevent progression and limit life-threatening organ dysfunction. To address these challenges, locally adaptable solutions are required, which permit individualized care based on available resources and the pretest probability of sepsis. This should facilitate affordable diagnostics which support risk stratification and prediction of likely treatment responses, and solutions for locally relevant outcome measures. For this purpose, global collaborative databases need to be established, using minimum variable datasets from routinely collected data. In summary, a "Think globally, act locally" approach is required.
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 | 0.273 | 0.326 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.003 |
| Bibliometrics | 0.009 | 0.006 |
| Science and technology studies | 0.004 | 0.009 |
| Scholarly communication | 0.015 | 0.021 |
| Open science | 0.010 | 0.018 |
| Research integrity | 0.006 | 0.019 |
| Insufficient payload (model declined to judge) | 0.003 | 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".