What Should Be the Focus of Treatment When Insomnia Disorder Is Comorbid with Depression or Anxiety Disorder?
Bibliographic record
Abstract
Insomnia is a significant, highly prevalent, persistent public health problem but often remains undiagnosed and untreated. Current treatment practices are not always evidence-based. When insomnia is comorbid with anxiety or depression, treatment often targets that comorbid condition with the expectation that improvement of the mental health condition will generalize to sleep symptoms. An expert panel of seven members conducted a clinical appraisal of the literature regarding the treatment of insomnia when comorbid anxiety or depression are also present. The clinical appraisal consisted of the review, presentation, and assessment of current published evidence as it relates to the panel’s predetermined clinical focus statement, “Whenever chronic insomnia is associated with another condition, such as anxiety or depression, that psychiatric condition should be the only focus of treatment as the insomnia is most likely a symptom of the condition”. The results from an electronic national survey of US-based practicing physicians, psychiatrists, and sleep (N = 508) revealed that >40% of physicians agree “at least somewhat” that treatment of comorbid insomnia should focus solely on the psychiatric condition. Whereas 100% of the expert panel disagreed with the statement. Thus, an important gap exists between current clinical practices and evidence-based guidelines and more awareness is needed so that insomnia is treated distinctly from comorbid anxiety and depression.
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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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".