Measures, markers, and mediators: Toward a staging system for clinical sepsis. A Report of the Fifth Toronto Sepsis Roundtable, Toronto, Ontario, Canada, October 25–26, 2000
Bibliographic record
Abstract
BACKGROUND: Sepsis is not a single disease but a complex and heterogeneous process. Its expression is variable, and its severity is influenced by the nature of the infection, the genetic background of the patient, the time to clinical intervention, the supportive care provided by the clinician, and a number of factors as yet unknown. The evaluation of effective therapies has been hampered by limitations in our ability to characterize the process and to stratify patients into more homogeneous groups with respect to pathogenesis. OBJECTIVES: To develop a taxonomy of markers relevant to clinical research in sepsis and to propose a testable candidate system for stratifying patients into more therapeutically homogeneous groups. DATA SOURCE: An expert roundtable discussion and a MEDLINE review using search terms "marker" and "sepsis." RESULTS: Markers provide information in one or more of three domains: diagnosis, prognosis, and response to therapy. More than 80 putative markers of sepsis have been described. All correlate with the risk of mortality (prognosis), yet none has shown utility in stratifying patients with respect to therapy (diagnosis) or in titrating that therapy (response). Their limitations arise from the challenges of establishing causality in a complex disease process such as sepsis and of stratifying patients into more homogeneous populations. The former limitation may be addressed through a modification of Koch's postulates to differentiate causality from simple association. The latter suggests the need for a staging system analogous to those used in other complex disease processes such as cancer. A candidate framework for such a system, based on the infection, the host response, and the extent of organ dysfunction (the IRO system) is described. CONCLUSIONS: Advances in the understanding and management of patients with sepsis will necessitate more rigorous approaches to disease description and stratification. Models should be developed, tested, and modified through clinical studies rather than through consensus.
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 | 0.017 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.010 | 0.011 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".