Longitudinal impact of depressive disorders on asthma control in adult patients
Bibliographic record
Abstract
Background: In recent years, a higher prevalence of depressive disorders (DD) has been observed in the adult asthma population. However, the longitudinal impact of DD on asthma control is unclear. This study aimed to assess the impact of DD on asthma control over a 4.3(±0.8)-year follow-up (f-up) in adult asthmatic patients. Methods: 641 consecutive patients (49 ±14 years old; 61% women) presenting to a tertiary asthma clinic underwent lung function testing and a sociodemographic, medical history (including the Asthma Control Questionnaire (ACQ)) and psychiatric (Primary Care Evaluation of Mental Disorders (PRIME-MD)) interview. At f-up, patients completed the ACQ and PRIME-MD, and reported emergency department (ED) visits, hospitalizations and doctor visits. Based on baseline and f-up DD status, patients were divided into four groups: No DD, Persistent DD, New DD and Remitted DD. Results: After adjustment for covariates (age, sex, %FEV1, smoking, ICS dose, baseline ACQ, f-up time), analyses revealed that compared to the No DD group (M=0.89), those in the New DD group (M=1.56, F=5.11, p<.0001) and Persistent DD group (M=1.30, F=2.61, p=.009) had worse ACQ scores at f-up with no difference seen in the Remitted DD group (M=1.02, F=1.08, p=.281). Also, the difference between the No DD and New DD groups was clinically significant (≥0.5). There were no associations between depression group and increased health service use (ED visits, hospitalizations, doctor visits) over the f-up (p’s>0.05). Conclusion: New and Persistent DD were prospectively associated with poorer asthma control independent of covariates. Greater efforts should be made to screen and treat depression in this population.
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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 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".