Identifying factors associated with high use of acute care in Canada: protocol of a population-based retrospective cohort study
Bibliographic record
Abstract
INTRODUCTION: High-cost users (HCUs) account for a small proportion of the population but use a disproportionately large share of healthcare resources. Although HCUs exist in all healthcare types, acute care is the most expensive type of service and the most significant contributor to expenditures among HCUs. This study aims to determine demographic, socioeconomic and clinical factors associated with being HCUs in adult patients (≥18 years) receiving acute care in Canada. METHODS AND ANALYSIS: This is a population-based analysis using a national linked dataset. Adult patients who had at least one interaction with acute care facilities each year from 2011 to 2014 were captured in the dataset, and those living in institutions or other collective residences were not covered. The primary outcome is HCU of acute care (yes/no), which is defined as whether a patient is within the top 10% of the highest acute care cost users in his/her province. Multilevel logistic regression will be used to identify factors associated with HCU and to examine the provincial variations of these identified risk factors. Sensitivity analyses investigating the influences of different high user definitions and missing data on the study results will also be performed. ETHICS AND DISSEMINATION: All researchers will follow the codes and rules set by Statistics Canada and the Research Data Centre and give priority to the confidentiality of the data during and after this study. The study findings will be published in peer-review journals and disseminated at academic conferences.
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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 | 0.014 | 0.010 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.005 | 0.007 |
| Science and technology studies | 0.007 | 0.002 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.015 | 0.003 |
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".