Statistical tools used for analyses of frequent users of emergency department: a scoping review
Bibliographic record
Abstract
OBJECTIVE: Frequent users represent a small proportion of emergency department users, but they account for a disproportionately large number of visits. Their use of emergency departments is often considered suboptimal. It would be more efficient to identify and treat those patients earlier in their health problem trajectory. It is therefore essential to describe their characteristics and to predict their emergency department use. In order to do so, adequate statistical tools are needed. The objective of this study was to determine the statistical tools used in identifying variables associated with frequent use or predicting the risk of becoming a frequent user. METHODS: We performed a scoping review following an established 5-stage methodological framework. We searched PubMed, Scopus and CINAHL databases in February 2019 using search strategies defined with the help of an information specialist. Out of 4534 potential abstracts, we selected 114 articles based on defined criteria and presented in a content analysis. RESULTS: We identified four classes of statistical tools. Regression models were found to be the most common practice, followed by hypothesis testing. The logistic regression was found to be the most used statistical tool, followed by χ2 test and t-test of associations between variables. Other tools were marginally used. CONCLUSIONS: This scoping review lists common statistical tools used for analysing frequent users in emergency departments. It highlights the fact that some are well established while others are much less so. More research is needed to apply appropriate techniques to health data or to diversify statistical point of views.
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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 | 0.128 | 0.417 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.013 | 0.014 |
| Bibliometrics | 0.075 | 0.061 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.008 | 0.008 |
| Open science | 0.005 | 0.006 |
| Research integrity | 0.005 | 0.003 |
| Insufficient payload (model declined to judge) | 0.011 | 0.002 |
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".