Prospective classification of functional dependence: Insights from machine learning and the Canadian Longitudinal Study on Aging
Bibliographic record
Abstract
Abstract Purpose Functional dependence is a multifactorial health condition affecting well-being and life expectancy. To better understand the mechanisms underlying this condition, we aimed to identify the variables that best prospectively classify adults with and without limitations in basic and instrumental activities of daily living. Methods A filtering approach was used to select the best predictors of functional status from 4,248 candidate predictors collected in 39,927 participants aged 44 to 88 years old at baseline. Several machine learning models using the selected baseline variables (2010-2015) were compared for their ability to classify participants by functional status (dependent vs. independent) at follow up (2018-2021) on a training dataset (n=31,941) of participants from the Canadian Longitudinal Study on Aging. The best performing model was then examined on a test dataset (n=7,986) to confirm sensitivity, specificity, and accuracy. Results Eighteen candidate baseline variables were identified as the best predictors of functional status at follow up. Logistic regression was the best performing model for classifying participants by functional status and achieved balanced accuracy of 81.9% on the test dataset. The absence of functional limitations at baseline, stronger grip strength, being free of pain and of chronic conditions, being a female, having a drivers license, and good memory were associated with greater odds functionally independence at follow-up. In contrast, older age, psychological distress, walking slowly, being retired, having one or more chronic conditions, and never going for walks were associated with greater odds of functional dependence at follow-up. Conclusion Functional status can be best prospectively estimated by health condition, age, muscle strength, short-term memory, physical activity, psychological distress, and sex. These predictors can estimate functional status over 6 years ahead with high accuracy. This early identification of people at risk of functional dependence allows sufficient time for the implementation of interventions aiming to delay functional decline.
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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 | 0.008 | 0.025 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".