COMPARISONS OF AUTOMATICALLY AND MANUALLY ACQUIRED GAIT SPEED IN KOREAN RURAL COMMUNITY DWELLING OLDER PEOPLE
Bibliographic record
Abstract
Gait speed (GS) of older people has been usually measured manually using stop watch. However, new simple devices allow automated GS measurement in clinic. We sought whether automatically measured GS differs from manually measured GS in distributions and associations with geriatric conditions including frailty index. A total of 260 community-dwelling older participants in Aging Study of Pyeongchang Rural Area (ASPRA) cohort had comprehensive geriatric assessment including automatically measured GS and manually acquired GS in December 2017. For automated GS, we used ultrasound sensor deployed at the start and finish point of 4 meter section. GS measured by automated device (mean 1.19 m/s, standard deviation [SD] 0.02 m/s) were faster than manually acquired GS (mean 0.87 m/s, SD 0.02 m/s, P < 0.001). Differences between two method was greater (P<0.001) in faster walking participants with manually acquired GS > 1 m/s (mean difference 0.31), compared to slower working participants with GS < 1m/s (mean difference 0.20). Automated GS and manually acquired GS correlated each other (R2 = 0.944, manually acquired GS = Automated GS * 0.697 + 0.044). GS measured by both method associated with frailty index, and both method could screen vulnerability (combining prefrailty and frailty defined by the Cardiovascular Health Study frailty criteria), without statistical differences in C-statistics (P = 0.203). GS measured manually was appeared to be slower when compared to automatically measured GS. For clinical use of automated GS, we may adjust reference values of GS that was originally measured manually to reflect differences between two instruments.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".