The history and evolution of immunoglobulin products and their clinical indications
Bibliographic record
Abstract
The history of providing antibodies to treat diseases began in the 19th century with the discovery of tetanus and diphtheria toxins and the demonstration that immunity to tetanus and diphtheria infections could be transferred by immune sera. Characterization of the mediators of this immunity resulted in the discovery that antibodies are proteins that can be isolated and used to protect against infectious diseases. Development of a method to isolate antibodies from human plasma that could be safely injected into people initiated the development of human gamma globulin preparations to provide antibodies to patients with inherited antibody deficiencies. To overcome the limitations imposed by intramuscular injection of gamma globulin, intravenous gamma globulin preparations were developed that began to be used in a wide variety of clinical conditions. Thus the original clinical indication for infection prevention was expanded to several other indications that employ large doses to suppress inflammatory and autoimmune disorders. The most recent development in immunoglobulin therapy is the production of concentrated immune globulins for subcutaneous injection. Home infusions of subcutaneous immunoglobulin are increasingly used to treat immunodeficient patients and are being studied for other clinical applications.
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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".