Variability in total serum IgE over 1 year in severe asthmatics
Bibliographic record
Abstract
Immunoglobulin E (IgE) is the treatment target of omalizumab, a monoclonal antibody indicated in the treatment of severe allergic asthma. Long-term variability of serum total IgE (sIgE tot ) in asthmatics remains poorly documented. In this prospective study, sIgE tot levels were measured over 1 year at 7 time points in 41 severe asthmatics treated with high-dose of inhaled corticosteroids and long-acting β 2 agonists. 33 patients were atopic based on at least one positive RAST to common aeroallergens. Patients were divided into three groups according to their baseline sIgE tot level: low (< 76 IU/mL; n = 10), intermediate (76–700 IU/mL; n = 20) or high (> 700 IU/mL; n = 11). Patients also completed the six-item Juniper Asthma Control Questionnaire (ACQ 6 ). The sIgE tot variability and factors predictive for this variability were studied, as well as ACQ 6 outcomes. The variation in sIgE tot level was mostly the consequence of between patient-variability, which represented 96%, 71% and 96% of the total variability in the low, intermediate and high sIgE tot subgroups, respectively. The residual within-patient variability was therefore limited. In 10/41 patients, sIgE tot levels increased or decreased, for at least one visit, beyond the predefined range of the subgroups to which they were assigned (< 76 IU/mL; 76–700 IU/mL; > 700 IU/mL). There was a significant but weak correlation between sIgE tot and ACQ 6 score over all time points (r = 0.15, p = 0.02), but sIgE tot failed to associate with severe exacerbation. sIgE tot decreased by 3% with any additional year of age for the whole group (p = 0.01) and increased by 5% per one unit of allergen exposure score in atopic patients (p = 0.002). In severe asthmatics, limited within-patient variability of sIgE tot levels was observed over 1 year as opposed to marked between-subject variability. sIgE tot decreases with age. Variation in sIgE tot weakly associates with asthma control but not with exacerbation.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".