Time in remission as an alternative outcome measure for rheumatoid arthritis: a 10-year prospective study of 2618 new users of anti-TNF
Bibliographic record
Abstract
OBJECTIVE: Achieving targeted disease activity (DA) is the primary therapeutic strategy in RA. Point measurements of DA are done at out-patient visits, however true DA between visits remains unobserved. This study sought to describe and validate a new outcome measure, i.e. time in remission (TIR). METHODS: Patients were enrolled in the Czech ATTRA-RA registry. TIR was calculated using linear interpolation of the DAS28-ESR determined at outpatient visits. Correlation coefficients were computed between TIR and DAS28-CRP, HAQ, Simple Disease Activity Index (SDAI), patient global assessment (PGA) and physician global assessment (PhGA). Using logistic regression, TIR was used as a predictor of remission (SDAI ≤3.3) and non-disability (HAQ <0.5). The predictive value of TIR was compared with point and sustained remission using the cross-validated area under receiver-operating curves. RESULTS: Since 2010, 2618 RA patients started anti-TNF therapy and were followed until 2020 or until treatment discontinuation. During the first 6 months of therapy, 56% of patients had no remission (TIR = 0), and 22% of patients reached sustained remission (TIR = 1), while 22% of patients had point remissions with 0 < TIR < 1. EULAR good responders and moderate/non-responders spent 64 ± 42% and 6 ± 18% of time in remission, respectively. The mean TIR grew during the follow-up and was correlated with DAS28-CRP, SDAI, HAQ, PGA, and PhGA (P < 0.0001). TIR at 3 and 6 months predicted remission (SDAI ≤3.3) and non-disability (HAQ <0.5) at 13 and 19 months better than point or sustained remission. CONCLUSIONS: TIR is an intuitive way of estimating unobserved DA between scheduled visits; its calculation only requires two consecutive DA values (https://www.medevio.cz/tir-calculator/). TIR is a valid predictor of RA outcomes.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".