Bibliographic record
Abstract
We need an instrument that is sensitive and specific Outcome assessments for flares in SLE trials have been both overly sensitive and non-specific, depending on the instrument being used. The article in this issue of Rheumatology by Thanou et al. [1], from a well-respected SLE centre, discusses proposed modifications to the classic Safety of Estrogen in Lupus Erythematosus National Assessment (SELENA)-SLEDAI Flair Index (SFI). The SFI is defined by an increase in the SLEDAI of 3 or more points (mild or moderate flare) or a 12 point increase (severe), a 0–3 visual analogue scale (VAS) with anchors for the physician global assessment (none, mild, moderate or severe flare) with an increase in 1 (for mild/moderate) or 2.5 (for severe) and adding NSAIDs or HCQ (for mild) or steroids, but no more than 0.5 mg/kg/day and/or adding a new immunosuppressant (for severe) [2, 3]. The SFI was developed prior to the use of new medications in SLE, such as MMF, belimumab and other biologics. Medications that were changed would be considered a flare but may be added as a standard of care, such as initiation of HCQ as usual care or changing one immunosuppressive to another due to side effects. It is important to try to determine clinically relevant flares for SLE clinical trials, so this article is an important step forward, adding an ability to separate mild to moderate flares and more concordance between flares and the physician global assessment.
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 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 | 0.003 | 0.025 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.008 | 0.006 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".