Regressive class models for machine learning algorithms to predict trajectories of repeated multinomial outcomes: an application to the activity of daily living of elderly data
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Due to the advancement of electronic data capturing, the amount of repeated categorical data being collected and stored has increased. This massive amount of data is complex and poses significant statistical challenges in methodology and computation. To analyze such big data, the divide and recombine method is commonly used. First, a large data set is partitioned into subsets, and each subset is analyzed separately. Then, the results are recombined in a manner that produces statistically valid output. However, available literature can only accommodate cross-sectional data. We propose a new simpler approach to analyze large, repeated categorical data using a joint modeling framework. In the proposed method, follow-up time is a natural conditioning variable that allows big data to be divided into subsets. Then, using the relationship between joint, marginal, and conditional probabilities, we can recombine the results in a statistically valid way. Several machine learning algorithms for cross-sectional data are extended for repeated outcomes to predict trajectories using the proposed framework. As an illustration, the proposed methodology is used to analyze repeatedly measured activity of daily living (ADL) data from the Health and Retirement Study (HRS), USA. We also check model performances under multiple machine learning algorithms using bootstrap simulations.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it