A Regression Tree for Identifying Risk Factors for Fear of Falling: The International Mobility in Aging Study (IMIAS)
Bibliographic record
Abstract
BACKGROUND: We determine the best combination of factors for predicting the risk of developing fear of falling (FOF) in older people via Classification Regression Tree (CaRT) analysis. METHODS: Community-dwelling older adults living in Canada, Albania, Brazil, and Colombia were from International Mobility in Aging Study (IMIAS). In 2014, 1,725 participants (aged 65-74) were assessed. With a retention rate of 81%, in 2016, 1,409 individuals were reassessed. Risk factors for FOF were entered into the CaRT: age, sex, education, self-rated health, comorbidity, medication, visual impairment, frailty, cognitive deficit, depression, fall history, Short Physical Performance Battery (SPPB), walking aid use, and mobility disability measured by the Nagi questionnaire. RESULTS: The classification tree included 12 end groups representing differential risks of FOF with a minimum of two and a maximum of five predictors. The first split in the tree involved impaired physical function (SPPB scores). Respondents with less than 8 in SPPB score and mobility disability had 82% risk of developing FOF at the end of 2-year follow-up. Between 23.2% and 82.3% of the risk of developing FOF in 2 years of follow-up were explained by only five variables: age, sex, self-rated health, functional impairment measured by SPPB, and mobility disability. In those with no functional impairment or mobility disability, levels of education, sex, and self-rated health were important predictors of FOF in the future. CONCLUSION: This classification tree included different groups based on specific combinations of a maximum of five easily measurable predictors with emphasis on impaired physical functioning risk factors for developing FOF.
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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 | 0.014 | 0.022 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.004 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".