Detailed cost analysis of care for survivors of severe sepsis
Bibliographic record
Abstract
OBJECTIVES: The objectives of our study were to accurately describe the costs and resources required to treat survivors of severe sepsis subsequent to hospital discharge and to determine what factors influenced these costs. DESIGN: Observational cohort study. SETTING: Three regional intensive care units. PATIENTS: Patients with severe sepsis admitted to one of three regional intensive care units in southern Alberta between April 1, 1996, and March 31, 1999. INTERVENTIONS: None. MEASUREMENTS AND MAIN RESULTS: Patients were identified using an intensive care unit research database; all survivors were followed prospectively for 3 yrs. Information on baseline patient characteristics, including acuity of illness (Acute Physiology and Chronic Health Evaluation II scores) and Charlson comorbidity scores, was collected. Costs considered included all episodes of inpatient and outpatient care and all physician claims. Of 787 patients who were admitted with severe sepsis, 502 survived to hospital discharge and were followed. Subsequent mean cost of care for years 1, 2, and 3 was CAN$20,855, $7,139 and $7,091, respectively. Using various regression models, the Acute Physiology and Chronic Health Evaluation II score and the Charlson comorbidity score were the only factors that consistently predicted higher healthcare costs in the first year after hospital discharge. Diabetes was the comorbid condition that best predicted subsequent cost. CONCLUSIONS: Cost of care for survivors of severe sepsis was highest in the first year after hospital discharge. Acuity of illness and patient comorbidity were the main determinants of cost. In assessing whether new therapeutic innovations for intensive care unit patients with severe sepsis are cost-effective, an accurate estimate of the cost of subsequent health care for survivors treated with and without the new intervention will be important.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| 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 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".