Abstract P120: Latent Group Trajectory Methods To Study Type 2 Diabetes Epidemiology: A Review Of The Literature
Bibliographic record
Abstract
Introduction: The progression of type 2 diabetes (T2D) is unique to each patient and can be depicted through individual temporal trajectories. Latent group trajectory methods (latent growth mixture models [LGMM] or latent class growth analysis [LCGA]) can be used to classify similar individual trajectories in a priori non-observed groups (latent groups), sharing common characteristics. Although increasingly used in the field of T2D epidemiology, many questions remain regarding the utilization of these methods. Objective: To review the literature of longitudinal studies using latent group trajectory methods among individuals with T2D. Methods: MEDLINE (Ovid), EMBASE, CINAHL and Web of Science were searched through August 25 th , 2021. Data were collected on the type of method used (LGMM or LGCA), characteristics of studies and quality of reporting using the GRoLTS-Checklist. Results: From the 4,694 citations screened, a total of 38 studies were included. The studies were published between 2010 and 2021. Total follow-ups ranged from 8 weeks to 11 years, with 95% (36 out of 38) studies with a follow-up of 1 year of more. The characteristics of studies are presented in Table 1. Regarding the quality of reporting, trajectory groups were adequately presented, however many studies failed to report important decisions made for the trajectory group identification. Many studies considered trajectory groups as exposures to a subsequent outcome. Yet, issues in relation with selection bias, immortal bias and residual confounding were suspected and poorly addressed. Conclusions: Although LCGA were preferred, the context of utilization, data sources and research questions were diverse and unrelated to the type of method used. We recommend authors to clearly report the decisions made in trajectory groups identification.
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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 | 0.017 | 0.066 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.006 |
| Bibliometrics | 0.034 | 0.034 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.006 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.009 | 0.002 |
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".