Differences in health outcomes for h<scp>igh‐need</scp>h<scp>igh‐cost</scp>patients across h<scp>igh‐income</scp>countries
Bibliographic record
Abstract
OBJECTIVE: This study explores variations in outcomes of care for two types of patient personas-an older frail person recovering from a hip fracture and a multimorbid older patient with congestive heart failure (CHF) and diabetes. DATA SOURCES: We used individual-level patient data from 11 health systems. STUDY DESIGN: We compared inpatient mortality, mortality, and readmission rates at 30, 90, and 365 days. For the hip fracture persona, we also calculated time to surgery. Outcomes were standardized by age and sex. DATA COLLECTION/EXTRACTION METHODS: Data was compiled by the International Collaborative on Costs, Outcomes and Needs in Care across 11 countries for the years 2016-2017 (or nearest): Australia, Canada, England, France, Germany, the Netherlands, New Zealand, Spain, Sweden, Switzerland, and the United States. PRINCIPAL FINDINGS: The hip sample across ranged from 1859 patients in Aragon, Spain, to 42,849 in France. Mean age ranged from 81.2 in Switzerland to 84.7 in Australia, and the majority of hip patients across countries were female. The congestive heart failure (CHF) sample ranged from 742 patients in England to 21,803 in the United States. Mean age ranged from 77.2 in the United States to 80.3 in Sweden, and the majority of CHF patients were males. Average in-hospital mortality across countries was 4.1%. for the hip persona and 6.3% for the CHF persona. At the year mark, the mean mortality across all countries was 25.3% for the hip persona and 32.7% for CHF persona. Across both patient types, England reported the highest mortality at 1 year followed by the United States. Readmission rates for all periods were higher for the CHF persona than the hip persona. At 30 days, the average readmission rate for the hip persona was 13.8% and 27.6% for the CHF persona. CONCLUSION: Across 11 countries, there are meaningful differences in health system outcomes for two types of patients.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".