Bibliographic record
Abstract
We value Dr. Caricchio’s and Dr. Criner’s appreciation of our commentary on the role of rheumatologists in the coronavirus disease 2019 (COVID-19) pandemic1. We applaud Temple University for collaborating in the care of hospitalized patients with COVID-19, many of whom are experiencing a cytokine storm syndrome (CSS)2. COVID-19 infection results in up to 20% of individuals requiring hospitalization for pneumonia, which can progress to acute respiratory distress syndrome, shock, and multiorgan dysfunction syndrome3. Many of the clinical and laboratory features of hospitalized patients with COVID-19 are reminiscent of a CSS1,4,5. Because CSS is frequently fatal, recognition of the CSS and early initiation of treatment directed specifically at the CSS is critical for reducing mortality … Address correspondence to Dr. R.Q. 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 | 0.005 | 0.042 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.034 | 0.041 |
| Insufficient payload (model declined to judge) | 0.012 | 0.009 |
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".