Bibliographic record
Abstract
We appreciate the interest expressed by Dr. C.A. Moura and colleagues in our editorial regarding the role of the rheumatologist during the coronavirus disease 2019 (COVID-19) pandemic1. The letter by C.A. Moura, et al emphasizes the need to understand mechanisms of disease underlying the more serious complications of COVID-19 infection, as well as to provide the best treatment possible to large numbers of seriously ill patients during a global pandemic2. Many of the treatments administered to date under crisis conditions in uncontrolled fashion have been based upon at least some mechanistic rationale as reflected in the Table 1 accompanying their letter. Well-designed randomized controlled trials (RCT) take time to design, undergo ethics board review, and enroll, and some of these are beginning to result. While the US National Institutes of Health–sponsored RCT with remdesivir has been reported to shorten the time to recovery and duration of hospitalization3, it is becoming increasingly apparent that the addition of immunomodulation will likely be required to forestall progression of respiratory failure, treat underlying vascular inflammation, and significantly affect survival1,4. Until definitive RCT can be completed, … Address correspondence to Dr. R. Cron, Children’s of Alabama, Division of Rheumatology, 1600 7th Ave. S., CPPN, Suite G10, Birmingham, AL 35233-1711, USA. Email: rcron{at}peds.uab.edu.
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 | 0.008 | 0.066 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.027 | 0.040 |
| Insufficient payload (model declined to judge) | 0.012 | 0.010 |
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".