Design of an Accelerated Longitudinal Cohort to Estimate Employment Trajectories using Multistate Models in a Population of Young Adults with Systemic Lupus Erythematosus
Bibliographic record
Abstract
Background: Systemic Lupus Erythematosus (SLE) is the most common form of Lupus. Objectives: To design a prospective accelerated longitudinal cohort (ALC) to study the employment trajectory of young-adult SLE patients. Methods: We analyzed a multicentre retrospective cohort of SLE patients using multi-state models to assess risk factors for unfavourable employment trajectories. Additionally, we conducted a simulation study to design an ALC that optimized the precision and bias for the probability of transitioning between employment states. Results: Due to insufficient data, model convergence in the retrospective cohort and simulation study was poor. Conclusions: ALCs are an attractive design for prospective studies; however, it is not an appropriate study design for the present population. For rare diseases such as SLE, the number of transitions is likely to be small. Future work should focus on expanding the objective to include additional populations who would benefit from interventions on undesirable employment trajectories.
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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 | 0.025 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".