Role of premedication to prevent reactions in cluster immunotherapy
Bibliographic record
Abstract
Introduction:The clinical efficacy of allergen immunotherapy is well documented, but the treatment always involves a risk of adverse reactions.Although premedication is not routinely performed, it has been shown to reduce the adverse reactions.Aim: To evaluate the contribution of premedication to prevent hypersensitivity reactions during cluster immunotherapy.Material and methods: 253 patients receiving a total of 290 cluster immunotherapy protocols to house dust mites, pollens, and venoms were recruited in the study.Patients were randomized into 5 groups according to the premedication status as follows: daily antihistamine, antihistamine only 2 h prior to injections, daily montelukast, combination of montelukast and antihistamine and the control group including patients without premedication.Patients were followed during up-dosing and maintenance phases of immunotherapy.Systemic and local reactions were reported.Results: Most of the patients were female (61.7%), the most frequent allergen was house dust mites (56.9%).67.2% of patients had premedication and 20.6% of patients had reactions during the up-dosing phase.Reactions were more frequent in patients who received pollen immunotherapy.The total frequency of the hypersensitivity reaction was significantly higher in the control group.When evaluated seperately, local reactions were more frequently observed in the control group, while no difference in the frequency of systemic reactions was detected.Conclusions: Our study suggests that the reaction risk is increased in pollen immunotherapy.Premedication does not seem to prevent the frequency or severity of systemic reactions.However premedication, daily AH intake in particular, decreases the frequency of local reactions.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 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".