The natural history of insomnia: Focus on prevalence and incidence of acute insomnia
Bibliographic record
Abstract
Despite Acute Insomnia being classified as a distinct nosological entity since 1979/1980 (ASDC/DSM III-R), there are no published estimates of its prevalence and incidence or data regarding transition to chronic insomnia or remission. This lack of data prevents an understanding of: a) the pathogenesis of insomnia and b) when and how treatment should be initiated. The aim of the present study was to provide such data from two community samples. Samples were recruited in the USA (n = 2861) and the North East of the UK (n = 1095). Additionally, 412 Normal Sleepers from the UK sample were surveyed longitudinally to determine prospectively incidence, transition, and remission rates for acute insomnia and assess whether the acute insomnia was a first episode, recurrent episode, or co-morbid with symptoms of other illnesses. The prevalence of acute insomnia was 9.5% (USA) and 7.9%(UK). The prevalence of three acute insomnia subtypes in the UK were; First-Onset Acute Insomnia 2.6%; Recurrent Acute Insomnia 3.8%; and 1.4% Co-morbid Acute Insomnia. The annual incidence of acute insomnia in the UK sample was between 31.2% and 36.6%. Remission rates fluctuated depending upon the definition of acute insomnia and whether the current episode was first-onset or recurrent. These findings provide preliminary insights into the natural history of insomnia. Such data will serve to inform how and when acute insomnia should be managed and whether such interventions may serve to diminish subsequent morbidity, particularly with respect to 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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| 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".