Co-inhibitory molecules: Controlling the effectors or controlling the controllers?
Bibliographic record
Abstract
Nearly forty years ago the concept was proposed that lymphocytes are negatively regulated by what are now called co-inhibitory signals. Nevertheless, it is only the more recent identification of numerous co-inhibitors and their critical functions that has brought co-inhibition to the forefront of immunologic research. Although co-inhibitory signals have been considered to directly regulate conventional T cells, more recent data has indicated a convergence between co-inhibitory signals and the other major negative control mechanism in the periphery that is mediated by regulatory T cells. Furthermore, it is now clear that lymphocytes are not the sole domain of co-inhibitory signals, as cells of the innate immune system, themselves controllers of immunity, are regulated by co-inhibitors they express. Thus, in order to better understand negative regulation in the periphery and apply this knowledge to the treatment of disease, a major focus for the future should be the definition of the conditions where co-inhibition controls effector cells intrinsically versus extrinsically (via regulatory or innate cells).
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.004 |
| Scholarly communication | 0.003 | 0.006 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".