Prospective Classification of Functional Dependence: Insights from Machine Learning and the Canadian Longitudinal Study on Aging
Bibliographic record
Abstract
Purpose: Functional dependence affects well-being and life expectancy. We identified baseline variables that best predict future limitations in basic and instrumental activities of daily living. Method: Using a filtering approach, we selected the best predictors from 4,248 candidate predictors for 39,927 Canadian Longitudinal Study on Aging participants aged 44–88 years and compared machine learning models trained on baseline data (2010–2015) on their ability to classify functional status (independent vs. dependent people) at follow-up (2018–2021) in a training set ( n = 31,941). We then tested the best model in a separate test set ( n = 7,986). Results: Eighteen variables best predicted functional status at follow-up. Logistic regression performed best, achieving 81.9% balanced accuracy in the test set. Protective factors included no baseline limitations, stronger grip, absence of pain or chronic conditions, female sex, having a driver’s license, and good memory. Risk factors included older age, psychological distress, slow walking, retirement, chronic conditions, and physical inactivity. Conclusion: Functional status can be predicted up to 6 years in advance using health, demographic, cognitive, and physical activity variables. Early identification may enable timely interventions to delay functional decline.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.004 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| 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".