Dr. Tselios, <i>et al</i> reply
Bibliographic record
Abstract
To the Editor: We thank Bachmeyer, et al 1 for their views on our recent article2 on mycophenolate mofetil (MMF) in refractory nonrenal manifestations of systemic lupus erythematosus (SLE). Concerning their observations on the definition of skin lesions, we have clearly indicated in the Materials and Methods section that the main indication for MMF was retrieved from the respective Systemic Lupus Erythematosus Disease Activity Index 2000 (SLEDAI-2K) element that was present at that particular date. The cutaneous variable of this index is defined in the SLEDAI-2K glossary as inflammatory type rash and includes acute, subacute, or chronic types of cutaneous lupus erythematosus (CLE). Because SLEDAI-2K was not designed to identify fluctuating activity within a particular organ system, we only reported the patients who achieved complete resolution by the end of 6 and 12 months, and … Address correspondence to Dr. M.B. Urowitz, University of Toronto Lupus Clinic, Centre for Prognosis Studies in the Rheumatic Diseases, Toronto Western Hospital, 399 Bathurst St., 1E-410B, Toronto, Ontario M5T 2S8, Canada. E-mail: m.urowitz{at}utoronto.ca
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 | 0.002 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.019 | 0.022 |
| Insufficient payload (model declined to judge) | 0.006 | 0.005 |
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".