Additional file 2 of Repopulation of T, B, and NK cells following alemtuzumab treatment in relapsing-remitting multiple sclerosis
Bibliographic record
Abstract
Additional file 2: Table S1 and supplementary figure legends. Increases in immune cell types or functions lacking statistically significant associations with risk of Gd+ and T2 lesions, with an exception for CD3+CD8+CXCR3+ T cells and T2 lesions (in bold type). Figure S1. Typical patterns of changes in lymphocyte and monocyte counts assessed every six months following alemtuzumab treatment in whole blood assessed in clinical TBNK assays. Figure S2. Expanded characteristics of Treg phenotypes in PBMC. Figure S3. Changes in naïve and memory CD4+ T cell subsets in whole blood following alemtuzumab treatment. Figure S4. Changes in cytokine secretion patterns in PBMC. Figure S5. Analyses of additional lymphocyte subsets stratified for active vs stable disease or presence and absence of secondary autoimmune disease. Figure S6. Lack of significant differences in percentages of CD4+CD25+CD127+foxP3- Teff cells stratified for patients with and without relapses (top three panels) or evidence of lesion activity on MRI (bottom three panels).
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 | 0.002 | 0.032 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.910 | 0.172 |
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".