Effectively detect dependence on benzodiazepines among community-dwelling seniors by asking only two questions
Bibliographic record
Abstract
Accessible summary • Benzodiazepines (BZDs) are mainly prescribed to treat insomnia and anxiety. But when someone uses BZDs continuously for more than 30 days, their usefulness in treating these conditions disappears. Research shows evidence of the same effectiveness as a placebo. BZDs also have side-effects, such as dizziness, memory impairment, reduced psychomotor coordination, falls, fractures and dependence. BZD consumption among seniors is frequent and it generally lasts more than 30 days. Detecting seniors addicted to BZD is not always easy because it is a prescription medication and also because trying to screen for this problem using the available instruments can take a long time. • Our aim was to give nurses a simple means of detecting the elderly person with a possible case of BZD dependence. Our results showed that if seniors answer ‘Yes’ to both questions ‘Have you tried to stop taking this medication?’ and ‘Over the past 12 months, have you noticed any decrease in the effect of this medication?’, there is a 97.1% possibility that they are BZD dependent. On the other hand, if they answer ‘No’ to one or both questions, they are 94.9% likely to not be BZD dependent. • We suggest that nurses and health providers ask seniors who use BZDs these two questions. When nurses get two affirmative (yes) answers, they should ask a doctor to check out the patient. With nurses and doctors working together, it is possible to prevent the quality of life of these seniors from getting worse by getting them into withdrawal programmes. Consumption of benzodiazepines (BZDs) is common among seniors. When used over a long period of time, BZDs can induce dependence. The present study aimed to equip nurses with valid screening questions for detecting BZD dependence among seniors, applicable to clinical practice and based on the DSM-IV-TR version. A random sample of 707 BZD users aged 65 years and over was screened for BZD dependence using the DSM-IV-TR criteria for substance dependence. To predict a diagnosis of BZDs dependence, sensitivity and specificity were computed for each pair of items. Results showed that an affirmative answer to ‘Have you try to stop taking this medication?’ and ‘Over the past 12 months, have you noticed any decrease in the effect of this medication?’ led to a sensitivity of 97.1% and a specificity of 94.9% to detect BZD dependence. Asking these two simple questions can be easily integrated into clinical practice and have considerable potential for identifying cases of BZD dependence.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".