Supplementary Material for: A Regression Tree Analysis to Identify Factors Predicting Frailty: The International Mobility in Aging Study
Bibliographic record
Abstract
Introduction: Frailty is a complex geriatric syndrome with a multifaceted etiology. We aimed to identify the best combinations of risk factors that predict the development of frailty using recursive partitioning models. Methods: We analyzed reports from 1,724 community-dwelling men and women aged 65–74 years participating in the International Mobility in Aging Study (IMIAS). Frailty was measured using frailty phenotype scale that included five physical components: unintentional weight loss, weakness, slow gait, exhaustion, and low physical activity. Frailty was defined as presenting three of the above five conditions, having one or two conditions indicated prefrailty and showing none as robust. Socio-demographic, physical, lifestyle, psycho-social, and life-course factors were included in the analysis as potential predictors. Results: 21% of pre-frail and robust participants showed a worse stage of frailty in 2014 compared to 2012. In addition to functioning variables, fear of falling (FOF), income, and research site (Canada vs. Latin America vs. Albania) were significant predictors of the development of frailty. Additional significant predictors after exclusion of functioning factors included education, self-rated health, and BMI. Conclusions: In addition to obvious risk factors for frailty (such as functioning), socio-economic factors and FOFs are also important predictors. Clinical assessment of frailty should include measurement of these factors to identify high-risk individuals.
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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 | 0.034 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.619 | 0.105 |
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".