Validation of a clinical prediction model for falls in community-dwelling older adults with COPD: A preliminary analysis
Bibliographic record
Abstract
BACKGROUND: People with chronic obstructive pulmonary disease (COPD) are at a higher risk of falls. This preliminary study aims to externally validate a previously developed clinical prediction model for falls in community-dwelling older adults with COPD. METHODS: This was a secondary analysis of a 12-month prospective cohort study. Older adults (≥60 years) with COPD, who reported a fall in the past year and/or had balance concerns, were tracked for 12-month future falls. Baseline predictors included 12-month history of ≥2 falls, total chronic conditions, and Timed Up and Go Dual-Task (TUG-DT) test scores. Model performance was assessed for discrimination (c-statistic), calibration (E:O, CITL, and calibration slope), and clinical value (decision curve analysis). RESULTS: %predicted = 47%). Of these, 35 (39%) reported ≥1 future fall, totaling 89 falls. The model demonstrated acceptable discrimination (c-statistic = 0.62, CI [0.51,0.72]), and calibration (E:O = 1, CITL = 0, and a calibration slope = 1). Decision curve analysis showed greater clinical value when using the prediction model compared to screening for fall history alone. CONCLUSIONS: A 12-month history of ≥2 falls, higher total chronic conditions, and worse TUG-DT test scores, predicts falls in community-dwelling older adults with COPD. Larger studies are needed before clinical application.
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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 | 0.025 | 0.043 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".