451 The Relationship Between Emotional Cognition and the Symptom Gap in Patients with Bronchial Asthma
Bibliographic record
Abstract
BACKGROUND: When symptoms are poorly controlled, patients with bronchial asthma may show a symptom gap: a cognitive divergence between the true severity of symptoms and the severity evaluated by the patients themselves. The aim of this study was to determine which factors (emotional cognition of the self and others) are associated with this symptom gap. METHODS: Forty-two patients with bronchial asthma, who were found with the Comprehensive Asthma Inventory (a bronchial asthma symptom questionnaire) to have psychosocial factors associated with a deep concern about the onset of asthma attacks, were studied by means of validated scales for alexithymia (the Toronto Alexithymia Scale-20) and for empathy (the Interpersonal Reactivity Index: IRI) and questions about how patients evaluate the severity of asthma. RESULTS: Of the patients, 42.5% showed a cognitive divergence regarding asthma symptoms. The scores for “perspective taking” on the IRI were significantly higher in patients who felt symptoms were less severe than they actually than in patients who felt symptoms were more severe than they actually were. No association was found between alexithymia and the symptom gap. CONCLUSIONS: The results show that empathy, the ability to understand the emotions of others, is associated with a symptom gap in patients with bronchial asthma and that high scores for “perspective taking” on the IRI may indicate problems of treatment and symptom control in asthma.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".