Patterns of inpatient acute care and emergency department utilization within one year post-initial amputation among individuals with dysvascular major lower extremity amputation in Ontario, Canada: A population-based retrospective cohort study
Bibliographic record
Abstract
INTRODUCTION: Lower extremity amputation (LEA) is a life altering procedure, with significant negative impacts to patients, care partners, and the overall health system. There are gaps in knowledge with respect to patterns of healthcare utilization following LEA due to dysvascular etiology. OBJECTIVE: To examine inpatient acute and emergency department (ED) healthcare utilization among an incident cohort of individuals with major dysvascular LEA 1 year post-initial amputation; and to identify factors associated with acute care readmissions and ED visits. DESIGN: Retrospective cohort study using population-level administrative data. SETTING: Ontario, Canada. POPULATION: Adults individuals (18 years or older) with a major dysvascular LEA between April 1, 2004 and March 31, 2018. INTERVENTIONS: Not applicable. MAIN OUTCOME MEASURES: Acute care hospitalizations and ED visits within one year post-initial discharge. RESULTS: A total of 10,905 individuals with major dysvascular LEA were identified (67.7% male). There were 14,363 acute hospitalizations and 19,660 ED visits within one year post-discharge from initial amputation acute stay. The highest common risk factors across all the models included age of 65 years or older (versus less than 65 years), high comorbidity (versus low), and low and moderate continuity of care (versus high). Sex differences were identified for risk factors for hospitalizations, with differences in the types of comorbidities increasing risk and geographical setting. CONCLUSION: Persons with LEA were generally more at risk for acute hospitalizations and ED visits if higher comorbidity and lower continuity of care. Clinical care efforts might focus on improving transitions from the acute setting such as coordinated and integrated care for sub-populations with LEA who are more at risk.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.004 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".