High-cost users after sepsis: a population-based observational cohort study
Bibliographic record
Abstract
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 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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.004 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".