Inhaled corticosteroids do not affect the antibody titer against the SARS-CoV-2 spike protein in BNT162b2 mRNA vaccinated patients
Bibliographic record
Abstract
OBJECTIVES: Oral corticosteroids reduce the antibody titer of the BNT162b2 mRNA vaccine against SARS-CoV-2. To date, the effect of inhaled corticosteroids on antibody titers is unknown. STUDY DESIGN: The design of this study is retrospective study. METHODS: We analyzed the relationship between the clinical features and total antibody titers against severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) spike protein in 320 subjects who had never been infected with Coronavirus disease 2019 (COVID-19) and were vaccinated the second time with the BNT162b2 mRNA vaccine between October 1 to December 28, 2021. RESULTS: Of the 320 subjects, 205 were treated with inhaled corticosteroids. The median antibody titer of patients treated with inhaled corticosteroids was 572 U/mL, which was significantly higher than that of patients treated without inhaled corticosteroids (454U/mL, P = 0.00258). The median antibody titers of smokers, men, and patients aged 65 years and over, were 315.5 U/mL, 385 U/mL, and 425.5 U/mL, respectively. These results are significantly lower than those of patients who never smoked, women, and patients aged less than 64 years (582 U/mL [P < 0.0001], 682.5 U/mL [P < 0.0001], and 717 U/mL [P < 0.0001], respectively). The multivariate analysis revealed that females and age were independent antibody titer-reducing factors (P = 0.0001 and P < 0.0001, respectively). CONCLUSIONS: The use of inhaled corticosteroids did not reduce the antibody titer against SARS-CoV-2 spike protein. Clinicians should continue treatment with inhaled corticosteroids if indicated.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".