Sensitivity and Specificity of the Minimal Chair Height Standing Ability Test
Bibliographic record
Abstract
BACKGROUND AND PURPOSE: Fall-risk screening instruments have been underutilized in clinical settings because of their lengthy administration time, need of cumbersome equipment, and lack of validation. The primary objective of this study was to assess the validity (sensitivity and specificity) of the Minimal Chair Height Standing Ability Test (MCHSAT). The secondary objective was to develop guidelines to provide physical therapists with best-practice recommendations that can easily be implemented in clinical practice. METHODS: A retrospective cohort study design was used in which falling history, major medical conditions, cognitive status (Mini-Mental State Examination), and level of independence (Independent Activities of Daily Living) were obtained for 167 community-dwelling older adults (mean age = 83.6 ± 7.3 years), residents of British Columbia, Canada. Participants MCHSAT performance was assessed using a chair whose seat height was modifiable by increments of 5 cm, starting at 47 cm and lowering after each successful attempt. Sensitivity and specificity of the MCHSAT at each chair height were calculated and plotted as a receiver operating characteristic curve. A model to identify participants with history of falls was developed using a forward logistic regression (Wald). RESULTS: Mean MCHSAT performance (cm) was significantly better for participants without history of falls (30.3 cm, 95% CI: 28.1-32.5 cm) than for those with history of falls (37.7 cm, 95% CI: 35.5-40.0 cm) and was the single risk factor associated with fall status (β= 1.087, P < .001). The optimal MCHSAT performance for identifying participants with history of falls was 34 cm (AUC = 0.72, 95% CI: 0.63-0.82). At this threshold, sensitivity and specificity values were 75% and 62%, respectively. DISCUSSION: Using 34 cm as the optimal performance, the MCHSAT correctly identified 75% of participants with history of falls and 62% of participants without history of falls. This provides evidence that the MCHSAT is a valid screening tool for use with an older Canadian population. CONCLUSION: As a simple and inexpensive testing instrument, the MCHSAT has potential to enhance the care of our elderly population, improve an understanding of patients' fall-risk status, and substantially reduce fall-related costs to the health care system.
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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 | 0.005 | 0.029 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".