Severe allergic asthma: Does alexithymia interfere with omalizumab treatment outcome?
Bibliographic record
Abstract
BACKGROUND: Alexithymia is among psychological factors reported to interfere with asthma management. Severe allergic asthma (SAA) is characterized by uncontrolled asthma despite maximal standard pharmacological treatment which can benefit from an add-on treatment with Omalizumab, an anti-IgE monoclonal antibody. OBJECTIVE: To evaluate if alexithymia influences the efficacy of omalizumab in SAA. METHODS: The total alexithymia score 20 (TAS 20) questionnaire allowed to detect alexithymia. SAA was monitored recording number of exacerbations, asthma control test (ACT) and asthma quality of life questionnaire (AQLQ) scores, as well as forced expiratory volumes in 1 second % (FEV1%) levels before starting omalizumab, 1 and 2 years after. RESULTS: The study was conducted on 18 patients; Group 1, TAS 20 ≥ 61, was of 2 males and 4 females with SAA and alexithymia, while Group 2 , TAS 20 ≤ 51, was of 8 males and 4 females, without alexithymia. Group 1 had a statistically significant decrease in asthma exacerbations "p = 0.004", while ACT "p = 0.008" and AQLQ scores statistically increased. FEV1 values increased but not statistically significantly. Group 2 had a highly statistically significant decrease in the number of exacerbations and a highly statistically significant increase of ACT "p < 0.0001", FEV1 "p = 0.008" and AQLQ scores. CONCLUSIONS: Regardless the presence or not of alexithymia, all patients with SAA obtained a marked improvement after starting treatment with omalizumab. Therefore alexithymia does not seem to influence the treatment outcome of omalizumab.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| 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".