Evaluating the predictive value of the in vitro Platelet Toxicity Assay (iPTA) for the diagnosis of hypersensitivity reactions to sulfonamide drugs: A prospective case‐control study.
Bibliographic record
Abstract
Drug hypersensitivity reactions (DHRs) are rare but potentially fatal types of adverse drug reactions that develop in susceptible patients following exposure to certain drugs including sulfonamides. Their diagnosis is challenging due to lack of safe and reliable test. The aim of this study was to evaluate the predictive value of the in vitro Platelet Toxicity Assay (iPTA) in diagnosis of DHRs to sulfonamides and to compare its performance to the conventional lymphocyte Toxicity Assay (LTA) test. Blood samples were obtained from 66 individuals (36 DHS‐sulfa patients and 30 controls) and LTA and iPTA were performed. Results were then analyzed to estimate the predictive value, sensitivity and agreement of the two tests. The concentration‐dependent toxicity was significantly greater in the cells from patients versus controls (p<0.05). The two tests had a high degree of agreement (correlation coefficient: R2 = 0.97). The iPTA was significantly more sensitive than the conventional LTA test in detecting the susceptibility of patient cells to in vitro toxicity (p<0.05). The iPTA has considerable potential as a diagnostic tool for DHS as it is cheaper to perform, requires no special reagents and has a greater sensitivity in detecting patients predisposed to DHRs to sulfonamides.
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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 | 0.004 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".