Bibliographic record
Abstract
OBJECTIVE: Recently, effector T-cell subpopulations have been identified that can be distinguished by expression of members of the TNF-R family: CD4+OX-40+ cells are CD4 helper-effector cells CD8+CD45RA+CD27 cells are CD8-killer-effector cells. We investigated whether these lymphocyte subsets were increased in the active phase of multiple sclerosis (MS). MATERIAL AND METHODS: Multiple colour immunofluorescence staining was performed on peripheral blood lymphocytes of 28 patients with active MS and of 29 healthy controls, followed by FACS analysis. RESULTS: Frequencies of CD8-killer-effector cells showed a wide interindividual range in both groups and percentages of CD4 helper-effector cells were low. No significant difference between the groups was observed for these subsets, but CD8+CD45RA-CD27 were increased in MS. In healthy individuals, CD4 helper-effector cells correlated with the total percentages of memory cells. Moreover, CD4+ and CD8 memory cells were strongly correlated. CONCLUSION: The here described recently identified effector CD4 and CD8 lymphocyte subpopulations were not increased in clinically active MS. It is however still possible that in MS, myelin-specific encephalitogenic cells reside within these subsets.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.410 | 0.333 |
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".