The validity of three fall risk screening tools in an acute geriatric inpatient population
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
AIM: We examined the validity of the Ontario Modified STRATIFY (OM) (St Thomas's Risk Assessment Tool in Falling Elderly Inpatients), The Northern Hospital Modified STRATIFY (TNH) and STRATIFY in predicting falls in an acute aged care unit. METHODS: Data were collected prospectively from 217 people presenting consecutively and falls identified during hospitalisation. RESULTS: Sensitivities of OM (80.0, 95% confidence interval (CI) 58.4 to 91.9%), TNH (85, CI 64.0 to 94.8%) and STRATIFY (80.0, CI 58.4 to 91.0%) were similar. The STRATIFY had higher specificity (61.4, CI 54.5 to 67.9%) than OM (37.1, CI 30.6 to 44.0%) and TNH (51.3, CI 44.3 to 58.2%). Accuracy (percentage of patients correctly classified as 'faller' or 'non-faller') was higher using STRATIFY (63.1, CI 56.5 to 69.3%) and TNH (54.4, CI 47.8 to 61.0%) than with OM (41.0, CI 34.7 to 47.7%, P < 0.0001). CONCLUSION: Screening tools have limited accuracy in identifying patients at high risk of falls.
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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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 it