Modified Vaccination Technique for Prophylactic and Therapeutic Applications to Combat Endogenous Antigen–Induced Disorders
Bibliographic record
Abstract
Public health can be protected most effectively through vaccination programmes. However, while presently available vaccination techniques protects the individual by provoking immune responses against exogenous antigens (ags), such as those associated with certain bacteria and viruses, they cannot protect against or treat mishaps caused by endogenous ag. Recently, Barabas and colleagues have developed a new vaccination method, called modified vaccination technique (MVT), which allows the presentation of disease causing agents in such a way as to initiate and maintain desired immune response outcomes even in the context of mishaps associated with endogenous ag. For example, in an experimental autoimmune kidney disease, the MVT downregulated/terminated pathogenic immune responses that were causing morphological and functional changes of the kidney. The MVT promises, with appropriate case-specific modifications, both preventative and curative applications for ailments, such as endogenous ag initiated mishaps (i.e. autoimmune diseases and cancer) and diseases caused by chronic infection, that are presently only treatable with drugs. To achieve specific immune responses, purified components of the vaccine (ag and antibodies) must be produced and assembled into immune complexes having the potential of inducing predetermined corrective immune response outcomes.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.003 |
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".