Validation of the STRATIFY Falls Risk Assessment Tool in a Japanese Acute Care Hospital Setting
Bibliographic record
Abstract
Patient falls are the most frequent adverse events that occur in a hospital. Prevention of inpatient falls is performed by a strategy to target patients at high risk for falls determined by a falls risk assessment system such as the STRATIFY tool. However, the performance of the STRATIFY tool in a Japanese hospital setting has not been determined. We tried to verify the performance of the STRATIFY tool for predicting falls in acutely hospitalized patients in Japan by a multi-center study. A total of 113,413 patients admitted to four acute cares national university hospitals during the period from April 2010 to March 2012 were studied. Inpatient falls per 1,000 patient-days varied from 1.42 to 2.92 in the four hospitals. The STRATIFY score was calculated on the basis of data extracted electronically from the hospital information system. Although the distribution of STRATIFY scores differed significantly among the four hospitals, logistic regression analysis and survival analysis showed that the proportion of high-risk patients who fell was significantly larger than the proportion of low-risk patients in all of the four hospitals. The odds ratio and hazard ratio for high-risk patients versus low-risk patients were 2.5 to 4.3 (combined estimate, 3.9 (95% confidence interval (95% CI), 2.1 to 7.6) and 1.8 to 5.1 (combined estimate, 3.1 (95% CI, 2.1 to 4.6)), respectively. The results suggest that the STRATIFY tool can be used as a screening tool to detect patients at high risk for falls in a Japanese acute care setting as used commonly in other countries.
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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 | 0.016 | 0.030 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".