Evidence for predictive validity of remission on long‐term outcome in rheumatoid arthritis: A systematic review
Bibliographic record
Abstract
OBJECTIVE: Remission is rapidly becoming a key end point in rheumatoid arthritis (RA) clinical trials, but its definition is not satisfactory. Although it is generally believed that achieving a state of remission will lead to better structural outcome, this has not been studied systematically. As part of an undertaking to redefine remission, the current review describes the relationship between remission and long-term structural outcome. METHODS: A systematic literature search of PubMed, EMBase, and The Cochrane Library intersected 3 groups of terms: RA, remission, and long-term outcome. The search identified 1,138 records, of which 14 were relevant to the research question. RESULTS: All of the studies included in this review showed a relationship between remission and long-term structural damage or disability. Patients that achieved a state of remission, defined in various ways, showed less deterioration of function and radiographic progression compared with patients who did not reach a state of remission. CONCLUSION: Patients who achieved a state of remission were less likely to show deterioration of function and radiographic progression compared with patients who did not reach a state of remission.
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 | 0.018 | 0.120 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.010 | 0.011 |
| Bibliometrics | 0.009 | 0.011 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.000 |
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".