The 2024 Phoenix Sepsis Score Criteria: Part 3, What About Using Stages of Sepsis in the Criteria?
Bibliographic record
Abstract
This Editorial Commentary written for Pediatric Critical Care Medicine (1) is “Part 3” of five focused comments (2–5) about the original Phoenix Sepsis Criteria work published in the Journal of the American Medical Association in February 2024 (6). “Part 1” contained an overview of the evolution in definition of sepsis and septic shock from representatives of the Society of Critical Care Medicine (SCCM) Pediatric Sepsis Definitions Taskforce (2). “Part 2” is a discussion on using “interventions” in the criteria (3); “Part 4” covers using “world orientated” criteria for sepsis (4); and “Part 5” completes the commentaries and discusses using “parsimony” in sepsis definition (5). Now, in “Part 3,” we comment on using “stages of sepsis” in the Phoenix criteria. For example, some patients with infection are excluded from being categorized as “sepsis” because they are not considered severe enough to require hospitalization but they would do well when they receive interventions such as respiratory support or antibiotics. Alternatively, the new definitions are at-risk of being too inclusive because they have the potential to include some patients with other well-described syndromes such as bronchiolitis or pneumonia. This range of categories generated vigorous discussion within the SCCM “Pediatric Sepsis Definitions Taskforce,” which we describe in order to provide better context for the issues raised.
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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 | 0.012 | 0.057 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.005 |
| 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".