Psychiatric co-morbidity and asthma: A pilot study utilizing a free use tool to improve asthma care
Bibliographic record
Abstract
PURPOSE: To assess the prevalence of co-morbid psychiatric disorders in asthmatic patients in a Western Canadian Regional Severe Asthma Center. METHODS: A prospective study was completed of patients evaluated through the Edmonton Regional Severe Asthma Clinic (ERSAC). A standardised evaluation, the Mini International Neuropsychiatric Interview (MINI) screen was used to identify possible psychiatric disorders. RESULTS: Twenty-four individuals with moderate to severe asthma, who presented for treatment at ERSAC, were recruited and underwent assessment with the MINI screen. The average patient age was 48 years (range 18-81 years). Nine patients were male and fifteen were female. Twenty subjects (83%) screened positive for a possible psychiatric co-morbidity using the MINI screen. The most common psychiatric co-morbidities identified were post-traumatic stress disorder (50% of the sampled population), depressive episode or persistent depressive disorder (42%), substance/alcohol abuse (33%), generalized anxiety disorder (335), manic episode (25%), agoraphobia (21%), panic disorder (21%) and obsessive-compulsive disorder (17%). Some individuals had more than one concomitant possible psychiatric co-morbidity identified by the MINI screen. CONCLUSIONS: Psychiatric co-morbidity was confirmed to be common in patients with moderate-severe asthma. In individuals with asthma, the MINI screen appeared to be a simple and useful clinical tool to screen for untreated/sub-optimally-managed psychiatric co-morbidities that may impact management.
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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 | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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".