Baseline Numbers of Circulating CD28-negative T Cells May Predict Clinical Response to Abatacept in Patients with Rheumatoid Arthritis
Bibliographic record
Abstract
OBJECTIVE: To evaluate the number of circulating CD28-negative (CD28-) T cells as a predictor of clinical response to abatacept in patients with rheumatoid arthritis (RA). METHODS: Peripheral blood CD28- T cell subsets were evaluated by flow cytometry at baseline in 32 patients with RA treated with abatacept. Receiver-operator curves were applied to examine the predictive value of T cell populations and to choose the cutoff for the best performance of the test. Remission was defined using the Disease Activity Score 28 based on C-reactive protein. RESULTS: The overall predictive values of the CD8+CD28- and CD4+CD28- cells for remission after 6 months of abatacept therapy were 0.802 (SE 0.078) and 0.743 (SE 0.089), respectively. Cutoff values of < 87 CD8+CD28- cells/μl and < 28 CD4+CD28- cells/μl had 80.0% sensitivity and 81.8% specificity (Fisher test: p = 0.001), and 60.0% sensitivity and 77.3% specificity (p = 0.043), respectively, for prediction of remission at 6 months. Patients having low baseline numbers of CD8+CD28- T cells had a more than 4-fold higher probability of achieving remission within 6 months than patients with higher levels of these cells. CONCLUSION: A simple laboratory measure, the baseline number of circulating CD28- T cells, predicted remission after 6 months of abatacept treatment in patients with RA.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".