Knee height to predict stature in North American Caucasian frail free-living elderly receiving community services.
Bibliographic record
Abstract
CONTEXT: Height is an important component of anthropometric assessment. Valid measures of height are difficult to obtain in the frail elderly. Equations to predict height, using knee height, were proposed for healthy but not for frail elderly. OBJECTIVE: The objectives of this study were to 1) develop and validate equations to predict height (measured and reported) in the frail elderly, 2) to verify the accuracy and reliability of equations, and 3) to compare predicted values with those predicted from existing equations for the healthy elderly. DESIGN AND SETTING: This is a secondary analysis of data from three cross-sectional studies and three randomized community trials in the Sherbrooke area, Quebec, Canada. PARTICIPANTS: Subjects (n=599) were Caucasian, aged 60 and over, and receiving community or Meals-on-Wheels services. ANALYSES: Variables associated with measured and reported heights were entered in multiple linear regression models (n = 409) to identify independent prediction factors. Reliability assessment and agreement analysis were performed with a sub-group of subjects (n=190). RESULTS: Knee height and age in men (R(2) = .718), and with the addition of weight and hip circumference in women (R(2) = .593), were identified as predictors of measured height. For reported height, knee height was a predictor in men (R(2) = .693), while weight was another predictor in women (R(2) = .540). These models predicted height just as well in the validation group (R(2) = .514 to .623). Errors of estimates ranged from +/- 3.31 cm to +/- 5.06 cm. Predicted values were closer to directly measured values in the frail elderly as compared to values obtained with equations in the healthy elderly which differed significantly. CONCLUSIONS: Equations were developed to predict measured and reported height in the frail elderly. These equations can be used when height cannot be measured directly or when postural problems (for measured height) or cognitive disorders (for reported height) can cause unreliable measurements.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| 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".