Bibliographic record
Abstract
Autoimmune diseases are among the most common chronic illness caused by a dysregulated immune response against self-antigens. Close to 5% of the general population in Western countries develops some form of autoimmunity, yet its underlying causes, although intensively studied, are still not fully known, and no curative therapies exist. It is well established that autoimmune diseases have common mechanisms and are caused by both genetic and non-genetic risk factors. One novel risk factor that can contribute to autoimmunity is somatic mutations, in a role parallel to their role in cancer. Somatic mutations are stochastic, de novo, non-inherited mutations. In this hypothesis, the persistent proliferation of self-reactive lymphocytes (that is usually hindered by a series of checkpoints) is permitted, due to somatic mutations in these expanding cells, allowing them to bypass multiple regulatory checkpoints, causing autoimmunity. This novel concept of the contribution of these mutations in non-malignant diseases has recently started to be explored. It proposes a novel paradigm for autoimmunity etiology and could be the missing piece of the autoimmunity puzzle.
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) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".