Advise non-pharmacological therapy as first line treatment for chronic insomnia
Bibliographic record
Abstract
### What you need to know Guidelines recommend non-pharmacological approaches, including cognitive behavioural therapy, as first line treatment for chronic insomnia in adults (box 1).12345 Yet, sedatives are commonly prescribed to treat insomnia. Over a quarter of a million people in the UK receive sedatives for more than a year based on estimates from a primary care survey in 2017.6 A drug survey in the UK reported 2.4 million adults had received, and had dispensed, one or more prescriptions for sedatives written in 2017-18.7 In a study of 386 457 US outpatient visits, prescription rates for benzodiazepines doubled from 3.8% (95% confidence interval 3.2% to 4.4%) in 2003 to 7.4% (6.4% to 8.6%) in 2015, including co-prescribing with other sedating medications.8 Sedatives include medications licensed for insomnia—for example, benzodiazepine receptor agonists (such as estazolam, temazepam, eszopiclone, zaleplon, zolpidem), dual orexin receptor antagonists (such as lemborexant, suvorexant), and melatonin receptor agonists (such as ramelteon)—as well as those used off-label (such as quetiapine, trazodone, diphenhydramine). Sedatives are associated with serious harm, including cognitive deficits, falls, confusion, hip fracture,91011 dependency,1213 and mortality.814 Overdose deaths involving benazodiazepines increased in the US from 1135 in 1999 to 8791 in 2015.14 Box 1 ### Guidelines promoting a non-pharmacological approach to insomniaRETURN TO TEXT
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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 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.005 | 0.004 |
| Insufficient payload (model declined to judge) | 0.105 | 0.043 |
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".