Bibliographic record
Abstract
Severe Collateral DamageThe magnitude of the health problem caused by doctors prescribing benzodiazepines and non-benzodiazepine agonists (1) in an uncritical way or to do patients a favor is revealed by this review.If 50% of the 230 million daily doses of these drugs are prescribed privately, even for patients who have statutory health insurance, it has to be assumed that the proportion of patients whose intake is problematic and lasts longer than four weeks is significantly underestimated with 20%.Unfortunately, of the many negative effects, the authors only address the problem of dependence.A study by a Canadian working group recently published in the British Medical Journal demonstrated a clear association between the prolonged intake of benzodiazepines and symptoms of Alzheimer's disease among elderly patients (2).Thus it could well be that, besides the common mistake of not recognizing arteriosclerotic encephalopathy, this explains the epidemic increase in the number of people being diagnosed with Alzheimer's disease-a reason that has nothing to do with extended life expectancy.It would not be the first time that an extensively researched idiopathic disease turns out to be a massive iatrogenic collateral damage.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".