Bibliographic record
Abstract
While the world of genetics has been dominated over the last decade by technological advances allowing the identification of common variants underlying the major complex diseases, it is increasingly clear that other genetic mechanisms are also involved in genetic susceptibility and resistance to disease. One understudied contender is microchimerism (maternal and foetal), resulting from bi-directional transfer of cells across the placental barrier in pregnancy. Data from several diseases suggest that elevated levels of microchimerism are associated with autoimmunity. Theories differ however on the role of these cells in the disease process. Some suggest that they increase genetic susceptibility while others suggest that these cells are effectors of the immune response, or that they represent the target of the immune response while another proposes that elevated levels in disease are caused by ongoing repair of damaged tissue. Intriguingly these semi allogeneic cells are tolerated in healthy individuals, albeit at a lower level than in disease scenarios and recent studies in cancer suggest that foetal microchimeric cells may provide surveillance and repair. Many questions remain to be answered about this new avenue of genetics. It is likely that as technology advances our understanding of, and ability to manipulate these cells for therapeutic gain, will push forward new frontiers in medicine.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.006 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.005 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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".