How Well Do Functional Assessments of Mobility and Balance Discriminate Fallers and Recurrent Fallers from Non-Fallers among Ambulatory Older Adults in the Community?
Bibliographic record
Abstract
PURPOSE: 1) To compare the ability of functional mobility and balance assessments in discriminating fallers from non-fallers and recurrent fallers from those with fewer or no falls. 2) To compare the discriminatory accuracy of cut-off scores specific to this study sample with that of cut-off scores proposed in the literature for community-dwelling older adults. METHODS: In a sample of 39 ambulatory older adults living independently in the community, fallers were identified on the basis of number of falls in the past year. Seven functional tests of mobility and balance were used to identify fallers and recurrent fallers on the basis of their fall history. RESULTS: Discrimination of fallers from non-fallers was poor: Only a high-level balance assessment significantly discriminated these groups (p=0.0498, area under the curve [AUC]=0.68). Four assessments significantly discriminated recurrent fallers from those with fewer or no falls (ps=0.006-0.009), but their discriminatory powers were not significantly different from one another (AUCs=0.77-0.80, p>0.05). For two assessments, cutoff scores based on the study sample enhanced discriminatory accuracy relative to the literature-based cutoff scores. CONCLUSIONS: To improve fall prediction for ambulatory community-dwelling older adults, future prospective studies should consider including high-level mobility and balance assessments and targeting cutoff scores to the level of function of this relatively high-functioning population.
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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 | 0.004 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| 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".