Manhattan Vision Screening and Follow-up Study (NYC-SIGHT): a nested cross-sectional assessment of falls risk within a cluster randomised trial
Bibliographic record
Abstract
BACKGROUND: To investigate the feasibility of using the Stopping Elderly Accidents, Deaths and Injuries (STEADI) Falls Risk Tool Kit during community-based eye health screenings to assess falls risk of participants enrolled in the Manhattan Vision Screening and Follow-Up Study (NYC-SIGHT). METHODS: Cross-sectional analysis of data from a 5-year prospective, cluster-randomised clinical trial conducted in affordable housing developments in New York City in adults age 40 years and older. Prescreening questions determined whether participants were at risk of falling. STEADI tests classified participants at low, moderate or high risk of falling. Multivariate logistic regression determined odds of falls risk of all enrolled participants. RESULTS: 708 participants completed the eye health screening; 351 (49.6%) performed STEADI tests; mean age: 71.0 years (SD±11.3); 72.1% female; 53.6% Black, non-Hispanic, 37.6% Hispanic/Latino. Level of falls risk: 32 (9.1%) low, 188 (53.6%) moderate and 131 (37.3%) high. Individuals age >80 (OR 5.921, 95% CI (2.383 to 14.708), p=0.000), had blurry vision (OR 1.978, 95% CI (1.186 to 3.300), p=0.009), high blood pressure (OR 2.131, 95% CI (1.252 to 3.628), p=0.005), arthritis (OR 2.29876, 95% CI (1.362 to 3.875), p=0.002) or foot problems (OR 5.239, 95% CI (2.947 to 9.314), p=0.000) had significantly higher odds of falling, emergency department visits or hospitalisation due to falling. CONCLUSION: This study detected a significant amount of falls risk in an underserved population. The STEADI Falls Risk screening questions were easy for eye care providers to ask, were highly predictive of falls risk and may be adequate for referral to occupational health and/or physical therapy.
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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 | 0.006 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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 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".