ABILITY OF THE PHYSIOLOGICAL PROFILE ASSESSMENT TO CLASSIFY FALLER TYPE: A PROSPECTIVE COHORT STUDY
Bibliographic record
Abstract
Abstract Accurately identifying older adults who will experience subsequent falls is important for the provision of secondary fall prevention. The purpose of this study was to determine the accuracy of the Physiological Profile Assessment (PPA) – a valid and reliable fall-risk assessment [1] – in predicting subsequent falls over a 12-month period in older adults who sought for medical attention after an index fall. Seven hundred thirty-seven community-dwelling adults, aged 70 years and older, who were seen at the Vancouver General Hospital Fall Prevention Clinic, completed the PPA at their initial visit. Falls over the subsequent 12 months were tracked prospectively via monthly falls calendars. All individuals received geriatric care at baseline. Binary logistic regressions were performed to determine the accuracy of classifying two prospective faller types: 1) no additional falls; 2) one or more additional fall(s). Baseline PPA, age, and sex were entered as independent variables. During the 12 month observation period, 345 participants had no additional falls (Age:81.3±6.6yrs;Female=251) and 392 fell one or more times (Age:82.3±6.5yrs;Female=230). The classification accuracy was 51.3% for those who had no additional falls and 64.8% for those with one or more additional fall(s) (Overall:58.5%;χ2=29.0;PPA:β=-0.21;Age:β=-0.01;Sex:β=-60). The PPA was not able to accurately differentiate between those who did and did not subsequently fall. Fall-risk assessment sensitivity and specificity should be improved in older adults seeking medical attention following an index fall to inform secondary fall prevention. [1] Lord SR, et al., 2003. Phys Ther.
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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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 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".