What causes international variations in length of stay: A comparative analysis for two inpatient conditions in Japanese and Canadian hospitals
Bibliographic record
Abstract
Hospital average length of stay varies considerably between countries. However, there is limited patient-level research identifying or discounting possible reasons for these differences. This study compares the length of stay of patients in Japan, where it is the longest in the OECD, and Canada, where length of stay is closer to the OECD mean. Administrative patient-level data, including age, gender, co-morbidities, intervention, discharge plan, outcome and length of stay were collected from two Japanese and two Ontario, Canada hospitals for two diagnoses: colorectal cancer surgery and acute myocardial infarction. Analyses examined linkages between patient characteristics, hospitals and countries and length of stay. When controlling for patient demographic characteristics, the incidence of co-morbidities and discharge plan practices, Japanese length of stay tended to be significantly longer than that in Canada for both diagnoses. Mortality rates were not significantly different; however, the readmission rate (28 days or less) for acute myocardial infarction was higher in the Canadian hospitals. The findings indicate that non-clinical factors contribute to sustained international differences in length of stay. These factors may include professional or cultural norms, differing payment schemes and access to long-term care facilities. The study also introduces a protocol that can be used for international patient-level comparisons that can enable effective policy and management learning.
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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 | 0.003 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.005 | 0.012 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| 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".