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 community-dwelling older people via Classification Regression Tree (CaRT) analysis. METHODS: Participants of International Mobility in Aging Study (IMIAS). Community-dwelling older adults living in Canada: Kingston (Ontario), Saint-Hyacinthe (Quebec); Albania (Tirana); and Latin America: Natal (Brazil), Manizales (Colombia). In 2014, 1,725 participants (aged 65- 74) were assessed. With a retention rate of 81%, in 2016, 1,409 individuals were reassessed. These risk factors for FOF were entered into the CaRT: age, sex, education, self-rated health, comorbidity, body mass index, medication, visual impairment, frailty, cognitive deficit, depression, fall history, and Short Physical Performance Battery (SPPB) total scores, 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 years follow-up. In respondents with no impaired physical functioning, the risk of developing FOF varied between 4% and 39.8%. Predictors in this branch of the tree were ‘low level of education’, ‘poor self-rated health’ and ‘living with non-spouse persons’. 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 | 0.013 | 0.023 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".