CNS volume 14 supplement 13 Cover and Front matter
Bibliographic record
Abstract
Most adults have difficulty sleeping at some point in their lives.These issues may manifest as problems falling asleep, staying asleep, or having nonrestorative sleep.When sleep issues begin to cause daytime dysfunction on a regular basis, they reach the status of an insomnia disorder.Individuals with insomnia have difficulties with concentration, fatigue, and mood.Insomnia is also associated with an elevated risk of major depressive disorder, panic disorder, substance abuse disorders, hypertension, and diabetes.Almost 50% of all cases of chronic insomnia are due to a mental disorder, with depressive illness being the most common.Other common causes of comorbid insomnia include chronic respiratory disease, chronic pain, degenerative neurological disease, and some medications.Evaluation of an individual with insomnia is comprehensive, and requires psychiatric, sleep, and medical evaluations including sleeprelated behaviors and thoughts, functional impairment, and underlying causes and comorbidities.There are a several therapies available for the treatment of primary insomnia, most notably cognitive-behavioral therapy and pharmacologic therapies.Generally, the greater the functional impairment a person experiences, the more there is to be gained from treatment.The decision about whether to treat insomnia and which treatments should be used, should take into account the risks and expected benefits of all treatment options.In management of insomnia related to mental or medical disorders, generally the principal disorder must be fully treated as part of the insomnia treatment plan.In this Expert Review Supplement, John W. Winkelman, MD, PhD, provides an introduction to sleep and describes the evaluation and workup of an individual with primary insomnia, including the use of a polysomnogram.Next, Andrew D. Krystal, MD, presents a case surrounding the decision of when to treat an individual for insomnia and determining which treatments are most effective.Finally, W. Vaughn McCall, MD, MS, presents a case on comorbid insomnia with an emphasis on the need to treat both insomnia and major 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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.657 | 0.430 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".