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
In a longitudinal study, data on different types of variables are often collected repeatedly over time. Some variables may be continuous, and some variables may be binary or times to an event of interest. Even for a single variable, data may be collected at different phases of the study with different characteristics. These different types of variables are typically associated or correlated, since they are measurements on the same individuals in the study. Analysis of data on each of these variables separately, ignoring other variables, may be inefficient and may also lead to biased results. Standard multivariate models with several correlated responses may not be easy to specify for different types of variables or when the models are nonlinear. Jointly modelling these variables simultaneously not only may be more efficient but may also reduce biases in parameter estimation. Statistical inference can then be based on the joint likelihood for all observed data. In this article, we briefly review several different types of joint models for longitudinal data. We focus on mixed effects models and likelihood methods for inference. We illustrate these joint models with datasets from HIV/AIDS studies. Journal of Statistical Research 2024, Vol. 58, No. 1, pp. 75-96.
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.
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 | 0.009 | 0.027 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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