High-cost users after sepsis: a population-based observational cohort study
Bibliographic record
Abstract
BACKGROUND: High-cost users (HCU) represent important targets for health policy interventions. Sepsis is a life-threatening syndrome that is associated with high morbidity, mortality, and economic costs to the healthcare system. We sought to estimate the effect of sepsis on being a subsequent HCU. METHODS: Using linked health-administrative databases, we conducted a population-based, propensity score-weighted cohort study of adults who survived a hospitalization in Ontario, Canada between January 2016 and December 2017. Sepsis was identified using a validated algorithm. The primary outcome was being a persistent HCU after hospital discharge (in the top 5% or 1% of total health care spending for 90 consecutive days), and the proportion of follow-up time since discharge as a HCU. RESULTS: We identified 927,057 hospitalized individuals, of whom 79,065 had sepsis. Individuals who had sepsis were more likely to be a top 5% HCU for 90 consecutive days at any time after discharge compared to those without sepsis (OR 2.24; 95% confidence interval [CI] 2.04-2.46) and spent on average 42.3% of their follow up time as a top 5% HCU compared to 28.9% of time among those without sepsis (RR 1.46; 95% CI 1.45-1.48). Individuals with sepsis were more likely to be a top 1% HCU for 90 consecutive days compared to those without sepsis (10% versus 5.1%, OR 2.05 [95% CI 1.99-2.11]), and spent more time as a top 1% HCU (18.5% of time versus 10.8% of time, RR 1.68 [95% CI 1.65-1.70]). CONCLUSIONS: The sequelae of sepsis result in higher healthcare costs with important economic implications. After discharge, individuals who experienced sepsis are more likely to be a HCU and spend more time as a HCU compared to individuals who did not experience sepsis during hospitalization.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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 teacher head, 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".