Bibliographic record
Abstract
To the Editor: We would like to thank Drs. Liu and Hu1 for their thoughtful comments regarding our recent publication.2 In response to the points made in their letter, we would like to make a few comments. We certainly agree that a practical, biologically based definition of a hypothetical “point of no return” in the preclinical maturation of rheumatoid arthritis (RA) autoimmunity, after which preventing RA onset becomes difficult to achieve, would be quite valuable in assigning at-risk individuals to specific intervention (interception) strategies. To address this key issue, a European Alliance of Associations for Rheumatology/American College of Rheumatology task force was recently established to develop risk stratification approaches across global population-based screening cohorts and arthralgia cohorts for individuals who are deemed to be at elevated risk of developing future RA. A thoughtful review of this topic has recently been published by international experts in the area, many of whom are participating in the task force.3 It is anticipated that within the next 1 to 2 years, the task force will have … Address correspondence to Dr. H. El-Gabalawy, RR149, 800 Sherbrook St, Winnipeg, MB R3A 1M4, Canada. Email: Hani.elgabalawy{at}umanitoba.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 | 0.004 | 0.035 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.028 | 0.032 |
| Insufficient payload (model declined to judge) | 0.006 | 0.006 |
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".