Use of the ‘STRATIFY’ falls risk assessment in patients recovering from acute stroke
Bibliographic record
Abstract
OBJECTIVES: To investigate the predictive validity and reliability of the STRATIFY falls risk assessment tool as applied to patients recovering from acute stroke. DESIGN: Prospective cohort study. SETTING: Six stroke rehabilitation units in the North of England. SUBJECTS: All patients with a diagnosis of acute stroke admitted to the participating stroke units during a 6-month study period. ASSESSMENT: on admission, falls risk (STRATIFY), disability (Barthel index), mobility (Rivermead mobility index), cognitive impairment (abbreviated mental test score) and visual neglect (Albert's test) were assessed. Then, STRATIFY was completed weekly and within 48 h of anticipated discharge. Consenting patients were contacted at 3 months after discharge to determine falls. OUTCOME MEASURES: Occurrence of a fall within 28 days of the baseline STRATIFY (in-patient study), falls in the first 3 months after discharge (post-discharge study) and falls during stroke unit stay (reliability study). RESULTS: From 387 patients admitted to the participating units during the study period, 225 contributed to the 28 day in-patient study, and 234 were followed up at 3 months after discharge. STRATIFY performed poorly in predicting falls in the first 28 days (sensitivity 11.3% and specificity 89.5%) and after discharge (sensitivity 16.3% and specificity 86.4%). Agreement was 'fair' between baseline and discharge scores (kappa = 0.263) and 'good' between the pre-hospital discharge score and that obtained in the week preceding discharge (kappa = 0.639). CONCLUSION: STRATIFY performed poorly as a predictor of falls in a heterogeneous population of stroke patients. There is a need for a disease-specific rather than a generic falls risk assessment tool.
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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 | 0.003 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 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".