Adjudication of Codes for Identifying Sepsis in Hospital Administrative Data by Expert Consensus*
Bibliographic record
Abstract
OBJECTIVES: Refine the administrative data definition of sepsis in hospitalized patients, including less severe cases. DESIGN AND SETTING: For each of 1928 infection and 108 organ dysfunction codes used in Canadian hospital abstracts, experts reached consensus on the likelihood that it could relate to sepsis. We developed a new algorithm, called AlgorithmL, that requires at least one infection and one organ dysfunction code adjudicated as likely or very likely to be related to sepsis. AlgorithmL was compared with four previously described algorithms, regarding included codes, population-based incidence, and hospital mortality rates-separately for ICU and non-ICU cohorts in a large Canadian city. We also compared sepsis identification from these code-based algorithms with the Centers for Disease Control's Adult Sepsis Event (ASE) definition. SUBJECTS: Among Calgary's adult population of 1.033 million there were 61,632 eligible hospitalizations. INTERVENTIONS: None. MEASUREMENTS AND MAIN RESULTS: AlgorithmL includes 720 infection codes and 50 organ dysfunction codes. Comparison algorithms varied from 42-941 infection codes to 2-36 organ codes. There was substantial nonoverlap of codes in AlgorithmL vs. the comparators. Annual sepsis incidence rates (per 100,000 population) based on AlgorithmL were 91 in the ICU and 291 in the non-ICU cohort. Incidences based on comparators ranged from 28-77 for ICU to 11-266 for non-ICU cohorts. Hospital sepsis mortality rates based on AlgorithmL were 24% in ICU and 17% in non-ICU cohorts; based on comparators, they ranged 27-38% in the ICU cohort and 18-47% for the non-ICU cohort. Of AlgorithmL-identified cases, 41% met the ASE criteria, compared with 42-82% for the comparator algorithms. CONCLUSIONS: Compared with other code-based algorithms, AlgorithmL includes more infection and organ dysfunction codes. AlgorithmL incidence rates are higher; hospital mortality rates are lower. AlgorithmL may more fully encompass the full range of sepsis severity.
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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 | 0.137 | 0.258 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.011 | 0.006 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.001 | 0.001 |
| 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".