Optimal determination of Physician Global Assessment of lupus disease activity: a pilot study
Bibliographic record
Abstract
The protracted diagnostic period and variable disease presentation not only complicate diagnosing SLE but also the epidemiologic study of it. Coupled with the remitting and relapsing nature of the disease and the challenges in managing it, clinical research in lupus requires careful attention to study design, control selection, temporality, and many often overlooked issues in the analysis phase. Between "big data" and the impressive advances in the basic sciences, it is tempting to either oversimplify methods to take advantage of "big data" or overcomplicate because the problem itself is complicated. As we revisit the building blocks of epidemiologic research, we will uncover opportunities to move epidemiology and clinical research forward in SLE. Why do we care about effect modification and what is it? Why can we not just adjust for everything that we want to? And perhaps, most importantly, going back to the very beginning and asking ourselves: does this matter? During this talk we will discuss issues relating to case identification methods, potential biases associated with control selection, and return to the basics of epidemiologic research. Although we shall discuss these issues in the context of environmental (nongenetic) factors, these concerns extend across the worlds of observational data analysis, can impact randomized trials, and are relevant for all types of exposures and outcomes.
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.
How this classification was reachedexpand
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 | 0.003 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".