Prevalence of Psychiatric Disorders among Patients Investigated for Occupational Asthma. An Overlooked Differential Diagnosis?
Bibliographic record
Abstract
RATIONALE: Up to one-third of patients assessed for occupational asthma (OA) do not receive a diagnosis of OA or any other medical disorder. Although several differential diagnoses are considered (e.g., rhinitis, chronic obstructive pulmonary disease), psychiatric disorders (many with somatic complaints that mimic asthma) are rarely considered or assessed. OBJECTIVES: To assess the prevalence of psychiatric disorders (mood and anxiety disorders and hypochondriasis) in patients suspected of having OA, and whether psychiatric morbidity increases the risk of not receiving any medical diagnosis. METHODS: A total of 219 consecutive patients (57% male; mean age, 41.8 ± 11.1 yr) underwent sociodemographic and medical history interviews on the control or specific inhalation testing day of their OA evaluation. The Primary Care Evaluation of Mental Disorders was used to assess mood and anxiety disorders, and the Whiteley Hypochondriasis Index was used to assess hypochondriasis. MEASUREMENTS AND MAIN RESULTS: A total of 26% (n = 50) of patients had OA; 25% (n = 48) had asthma or work-exacerbated asthma; 14% (n = 28) had another inflammatory disorder; 13% (n = 26) had a noninflammatory disorder; and 22% (n = 44) did not receive any medical diagnosis. A total of 34% (n = 67) of patients had a psychiatric disorder: mood and anxiety disorders affected 29% (n = 57) and 24% (n = 46) of the sample, respectively, and 7% (n = 12) had scores on the Whiteley Hypochondriasis Index indicating hypochondriasis. Hypochondriasis, but not mood or anxiety disorders, was associated with an increased risk of not receiving any medical diagnosis (adjusted odds ratio, 3.92; 95% confidence interval, 1.18-13.05; P = 0.026). CONCLUSIONS: Psychiatric morbidity is common in this population, and hypochondriasis may account for a significant proportion of the "undiagnosable" cases of patients who present for evaluation of OA.
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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 | 0.000 | 0.004 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".