Self‐report of memory and affective dysfunction in association with medication use in a sample of individuals with chronic sleep disturbance
Bibliographic record
Abstract
Benzodiazepines produce memory disturbance after acute administration. It is not clear whether chronic use of benzodiazepines is hazardous to memory processes. Epidemiological data indicate that a large proportion (10-30 per cent) of individuals with sleep dysfunction take hypnotic aids for a year or longer. The purpose of the present study was to evaluate self-reported memory dysfunction in a sample of individuals who considered their sleep disturbance sufficiently severe to seek investigation in sleep clinics. It was hypothesized that individuals taking benzodiazepines for sleep would report greater perceived everyday memory failures than individuals taking other sleep aids or no medication. Questionnaires were given to 368 individuals referred into the study by investigators in six sleep disorders clinics. All respondents completed a lengthy (700-item) questionnaire, which included scales assessing memory difficulties, affective status and sleep disturbance. Respondents also reported any medication use for sleep problems and duration of use of the current drug. Information on medication use was reported by 289 participants. Fifty-six per cent of respondents reported using some form of psychoactive medication (antidepressants, benzodiazepines, Zopiclone). Twenty-two per cent reported using no medication. Analysis of covariance showed that these medications had no detectable effect on subjective memory difficulties during chronic use, F(4,226)=1.34, p=0.25. Copyright 2000 John Wiley & Sons, Ltd.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".