Long-Term Benzodiazepine Therapy Does Not Result in Brain Abnormalities
Bibliographic record
Abstract
Studies on the association between long-term benzodiazepine use and brain abnormalities have yielded conflicting results. The computed tomographic (CT) scans of 20 long-term users of benzodiazepine (65% men; mean age +/- SD [range], 42 +/- 12.1 years [23-59]; mean daily benzodiazepine dose [diazepam equivalents], 19.5 +/- 16.2 mg [2.5-70]; mean cumulative benzodiazepine exposure, 55.2 g [1.8-198]) were compared with 36 age- (+/-3 years) and sex-matched controls. Controls were prospectively recruited from 96 patients attending a neurology clinic and were interviewed to screen for alcohol and substance use disorders and other conditions possibly leading to brain atrophy. Three neuroradiologists blindly assessed each CT scan for atrophy and measured ventricles (V1, V2, V3), sulci, fissures, cisterns, and folia. Reliability among observers ranged from 0.92 to <0.1, in which case deleting one observer increased all reliabilities to >0.45. No difference in atrophy was found between benzodiazepine users and controls. V1 measures were significantly higher for benzodiazepine users than for controls (mean +/- SD, 12.1 +/- 1.3 vs. 11.1 +/- 2.0;p = 0.02), but measures of third and fourth largest sulci were significantly higher in controls than in benzodiazepine users. Right third and fourth largest sulci (mean +/- SD), respectively, were the following: controls, 0.72 +/- 0.4 and 0.74 +/- 0.7; benzodiazepine users, 0.51 +/- 0.3 and 0.46 +/- 0.3 (p < 0.02). Left third and fourth largest sulci, respectively, were the following: controls, 0.77 +/- 0.6 and 0.65 +/-0.3; benzodiazepine users, 0.53 +/- 0.3 and 0.5 +/- 0.3 (p < 0.02). Long-term benzodiazepine therapy does not result in brain abnormalities that can be demonstrated on CT scans.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".