A systematic review and meta-analysis of studies using the STRATIFY tool for prediction of falls in hospital patients: how well does it work?
Bibliographic record
Abstract
BACKGROUND: STRATIFY is a prediction tool developed for use in for hospital inpatients, using a 0-5 score to predict patients who will fall. It has been widely used as part of hospital fall prevention plans, but it is not clear how good its operational utility is in a variety of settings. OBJECTIVES: (i) to describe the predictive validity of STRATIFY for identifying hospital inpatients who will fall via systematic review and descriptive analysis, based on its use in several prospective cohort studies of hospital inpatients; (ii) to describe the predictive validity of STRATIFY among inpatients in geriatric rehabilitation via meta-analysis and (iii) in turn, to help practitioners and institutions wishing to implement interventions to prevent in-hospital falls. METHODS: a systematic literature review of prospective validation studies of STRATIFY for falls prediction in hospital inpatients. For inclusion, studies must report prospective validation cohorts, with sufficient data for calculation of sensitivity (SENS), specificity (SPEC), negative and positive predictive value (NPV and PPV), total predictive accuracy (TPA) and 95% confidence intervals (CI). We performed meta-analysis using precision-weighted fixed- and random-effects models using studies that evaluated STRATIFY among geriatric rehabilitation inpatients. MEASUREMENTS: key features of the patient population, setting, study design and numbers of falls/fallers were abstracted. SENS, SPEC, PPV, NPV, TPA and 95% CI were reported for each cohort. Pooled values and chi-squared test for homogeneity were reported for a meta-analysis of studies conducted in geriatric rehabilitation settings. RESULTS: forty-one papers were identified by the search, with eight ultimately eligible for inclusion in the systematic review and four for inclusion in the meta-analysis. The predictive validity of STRATIFY, using a random-effects model, for the four studies involving geriatric patients was as follows: SENS 67.2 (95% CI 60.8, 73.6), SPEC 51.2 (95% CI 43.0, 59.3), PPV 23.1 (95% CI 14.9, 31.2), NPV 86.5 (95% CI 78.4, 94.6). The Q((3)) test for homogeneity was not significant for SENS at P = 0.36, but it was significant at P < 0.01 for SPEC, PPV and NPV. TPA across all four studies varied from 43.2 to 60.0. CONCLUSION: the current study reveals a relatively high NPV and low PPV and TPA for the STRATIFY instrument, suggesting that it may not be optimal for identifying high-risk individuals for fall prevention. Further, the study demonstrates that population and setting affect STRATIFY performance.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.004 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".