Bibliographic record
Abstract
We thank Dr. So, et al 1 for the interest in our letter2 and for sharing the results about the coronavirus disease 2019 (COVID-19) in patients with systemic lupus erythematosus (SLE) in Hong Kong1. We agree that the quantification of the risk of infection with severe acute respiratory coronaviruses-2 (SARS-CoV-2) in patients with SLE is a major concern. This is even more true in light of the recently published data from Mathian and colleagues3, who analyzed the course of COVID-19 in a case series of 17 patients with SLE. Of these, 13 (76%) developed interstitial pneumonia, complicated by respiratory failure in 11 (65%) and acute respiratory distress syndrome in 5 (29%). … Address correspondence to Dr. E.G. Favalli, Division of Clinical Rheumatology, ASST Gaetano Pini-CTO Institute, Via Gaetano Pini 9, 20122 Milan, Italy. Email: enniofavalli{at}me.com.
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 | 0.003 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.022 | 0.024 |
| Insufficient payload (model declined to judge) | 0.007 | 0.007 |
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".