Diagnostic Potential of Coagulation-Related Biomarkers for Sepsis in the Emergency Department: Protocol for a Pilot Observational Cohort Study
Bibliographic record
Abstract
BACKGROUND: Between 75% and 80% of patients with sepsis arrive in the hospital through the emergency department. Early diagnosis is important to alter patient prognosis, but currently, there is no reliable biomarker. The innate immune response links inflammation and coagulation. Several coagulation -related biomarkers are associated with poor prognosis in the ICU. The role of coagulation biomarkers to aid in early sepsis diagnosis has not previously been investigated. The objective of our study is to determine the individual or combined accuracy of coagulation and inflammation biomarkers with standard biochemical tests to diagnose adult septic patients presenting to the emergency department. METHODS: in the Emergency Department is a prospective, observational cohort study with a target enrolment of 250 suspected septic patients from two Canadian emergency departments. The emergency physicians will enroll patients with suspected sepsis. Blood samples will be collected at two time points (initial presentation and 4 hr following). Patients will be adjudicated into septic, infected, or not infected status in accordance with the Sepsis-3 definitions. Patient demographics, cultures, diagnosis, and biomarkers will be reported using descriptive statistics. Optimal cut off values with sensitivity and specificity for each biomarker will be determined using C-statistics to distinguish between septic and nonseptic patients. Stepwise multiple logistic regression analysis with exclusion of nonsignificant covariates from the final model will be used to establish a panel of biomarkers. CONCLUSIONS: Our protocol describes the processes and methods for a pragmatic observational biomarker study in the emergency department. This study will seek to determine the potential diagnostic importance of early coagulation abnormalities to identify additional tools for sepsis diagnosis.
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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 | 0.046 | 0.036 |
| Meta-epidemiology (narrow) | 0.004 | 0.002 |
| Meta-epidemiology (broad) | 0.004 | 0.003 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.017 | 0.005 |
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".