Bibliographic record
Abstract
In 2010, remission was redefined1. The set of 4 proposed criteria — 2 clinical trial criteria and 2 clinical practice criteria — have since been the subject of several validation studies. Rheumatology researchers have shown that clinical trial criteria requiring a tender joint count, swollen joint count, C-reactive protein (CRP) and patient global assessment ≤ 1 or a Simplified Disease Activity Index (SDAI) ≤ 3.3 perform well in both trial and clinical-practice settings2,3,4,5,6. Fewer data are available on the performance of the practice-based criteria, which exclude the CRP criterion to increase feasibility. Shahouri, et al reported good agreement between the trial and practice-based criteria, and both Lillegraven and Zhang reported comparable predictive validity of the practice-based criteria in observational datasets as compared to trial datasets from which they were developed2,3,6. Reported prevalence rates of remission according to the new criteria range between 5% and 25%2,3,4,5,6,7,8,9, a marked decline compared to reported DAS-based remission rates between 25% and 50%, and even one instance of 90%10. We now refer to Disease Activity Score (DAS)-based remission as minimal disease activity. Despite these developments, there are some concerns with the new criteria, especially regarding the requirement of patient global assessment (PtGA) ≤ 1. Both Masri, et al and Studenic, et al independently report that the PtGA requirement decreases the specificity of the criteria, especially with the report of noninflammatory problems like low back pain, contributing to false-negative cases11 …
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 | 0.010 | 0.026 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".