Adherence to the treatment of patients with bronchial asthma
Bibliographic record
Abstract
Background: Low adherence to the treatment is one of the main problems which hampers the achievement of asthma control. Aim: To study psychological characteristics of asthmatics, affecting adherence to treatment. Methods: 141 patients with mild (n=28), moderate (n=86) and severe asthma (n=27) were examined. The mean age was 44±1.2 years old, the mean duration of the disease was 6±0.8 years. The questionnaire of the assessment of adherence to treatment at asthma, Toronto Alexithymia Scale – TAS (G.Taylor, 1984), and self-questionnaire for identification of depression CES-D (M.M.Weissman et al., 1977) were used. Results: While self-filling questionnaire of the assessment of adherence to treatment the patients overestimated their compliance (self-assessment of compliance index was 100%). Therefore, we used the data from interviews with patients. The patients were divided into 2 groups according to adherence to treatment. The 1st group included 34 patients with high adherence to treatment. The 2nd group consisted of 107 patients who violated the regime of treatment and did not follow the prescriptions. The 2nd group significantly differed from the 1st one by low mean values of "compliancy index" (58.1±7.3 vs. 78.4±12.3%), high indices of TAS (66.2±2.3), low level of education and unemployment (14 vs. 3%). All patients had high safety needs (the mean index was 87.6±6.3%). The degree of adherence to treatment inversely correlated with asthma severity (Spearman9s R=-0,68), the duration of the disease (R=-0,54), the level of depression (R=-0.59). Conclusion: The severity and duration of asthma, alexithymia, poor education and unemployment have most influence on the degree of adherence to treatment in patients with 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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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) | 0.001 | 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".