Response to Comment on: Tritt et al. (2007) Functional Waning of Naturally Occurring CD4+ Regulatory T-Cells Contributes to the Onset of Autoimmune Diabetes: <i>Diabetes</i> 57:113–123, 2007
Bibliographic record
Abstract
We thank Thomas et al. (1) for their insightful comments regarding the functional dynamics of CD4+Foxp3+ regulatory T-cells (Tregs) in their model of type 1 diabetes. An impressive array of studies in the literature establishes that CD4+Foxp3+ Tregs play a central, master-switch role in peripheral tolerance in the NOD mouse model of spontaneous type 1 diabetes (2,3). A central question is whether the onset of spontaneous disease in NOD mice results from a decline in regulation over time or from uncontrollable activity of self-reactive T-cells. Type 1 diabetes may reflect subtle, functional deficiencies in regulatory T-cells, thus allowing the diabetogenic process to unfold. In our study (4), we attempted to determine whether temporal, quantitative, or qualitative defects in CD4+Foxp3+ Tregs contribute to spontaneous type 1 diabetes. The BDC2.5 mouse model contains a highly pathogenic CD4+ T-cell repertoire and represents a unique system to study …
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 | 0.003 | 0.015 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.005 | 0.002 |
| Research integrity | 0.029 | 0.029 |
| Insufficient payload (model declined to judge) | 0.021 | 0.022 |
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".