Bibliographic record
Abstract
It has been more than 50 years since the first reports of penicillin‐ and methyldopa‐induced haemolytic anaemias emerged. Since these initial observations in 1966, that high‐dose intravenous penicillin can result in a penicillin‐antibody‐dependent immune haemolytic anaemia and patients receiving α ‐methyldopa developed haemolytic anaemia similar to warm autoimmune haemolytic anaemia, cases of patients having a drug‐induced haemolysis has expanded greatly to include a myriad of different drugs. Indeed, there are now well over 100 drugs that have been described as causing immune‐mediated haemolysis. The history of drug‐induced immune haemolytic anaemia's can be subdivided into two different chapters: (1) The penicillin and methyldopa chapter and (2) the cephalothins and cefotetan chapter. In this review, these two chapters in the history of drug‐induced immune haemolytic anaemias will be dissected, including the different mechanisms of haemolysis that have been proposed. These include so‐called hapten, immune‐complex, drug adsorption and autoimmune mechanisms. The biochemical and immunological mechanisms of drug‐induced immune haemolytic anaemias will be discussed as well as important considerations in the approach to the laboratory investigation of these conditions in the blood bank and reference laboratories. An approach to the use of drug metabolites as well as a hypothesis as to how ‘non‐specific’ drug adsorption occurs and how it can be prevented will be presented. The objective of this review will be to provide an up‐to‐date understanding of the types of drug‐induced immune haemolytic anaemias, their differentiation in the laboratory and the biochemistry and immunology underlying these conditions.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".