Bibliographic record
Abstract
Immune checkpoint receptors are expressed by cells of the immune system and lead to reduced or absent function, which physiologically limits autoimmunity. These receptors are also exploited by malignant cells to maintain immune tolerance and evade destruction. Monoclonal antibodies targeting immune checkpoints have revolutionized oncology, with potential long-lasting clinical response, even in the setting of metastatic solid tumors. For example, in the past, metastatic melanoma signalled certain death; now, remission is possible. The primary targets of current pharmacotherapy are cytotoxic T-lymphocyte-associated protein 4 (CTLA-4) and the programmed cell death protein 1 (PD-1) and its ligand (PD-L1). Today, half of all patients with metastatic disease are eligible to receive immune checkpoint inhibitor (ICI) therapy. As of December 2021, there were eight approved agents available for 17 malignancies, and more than 1,000 clinical trials have been conducted to explore these agents in adjuvant and maintenance settings. The immune activation that underlies ICI therapy and the persistence of clinical response beyond the pharmacologic half-life also explain the toxicities that have been observed. Immune-related adverse events (irAEs) from ICI therapy have been shown to occur in virtually every organ system. They manifest at varying times during treatment, sometimes occurring after its discontinuation. Interestingly, the presence of these adverse events (AEs) is related to the immune system’s degree of self-tolerance and predicts patient response to this treatment modality. Endocrinopathies are some of the most common irAEs, occurring in 15–40% of patients; however, they have posed challenges for clinicians as they are difficult to diagnose due to diverse and non-specific manifestations. In contrast to other irAEs, endocrinopathies do not respond to high-dose glucocorticoids and they are permanent. Steroid treatment has been shown to have no effect on the disease severity or the likelihood of resolution. Fortunately, when diagnosed appropriately, ICI-associated endocrinopathies are easy to treat, do not necessitate treatment discontinuation, and have an excellent prognosis.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 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".