Use of antipsychotics and benzodiazepines for dementia: Time for action? What will be required before global de-prescribing?
Bibliographic record
Abstract
Comparing how nations including the UK, USA, Canada, Australia and others have made attempts aimed at improving the care and treatment of dementia patients can provide useful insights into methods that prove successful. The UK-based 2009 Banerjee Report provided international leadership in addressing treatment and practices for dementia patients with an aim to reduce prescribing of antipsychotic drugs. A historical account of the different government policies and developments with the similar aims of de-prescribing are examined. Using Australia as one example, different national strategies are discussed in the context of those that have been tried and failed. In addition, policies that have successfully reduced the controversial current practices of overprescribing antipsychotics or related psychotropic drugs for dementia patients are presented. The evidence overwhelmingly indicates such treatments only exacerbate the disease or precipitate death – giving justification to the recent call for use of chemical restraints such as antipsychotics to be included under ‘Elder Abuse’ when considering law reform necessary to regulate compliance .
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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 | 0.016 | 0.048 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.007 |
| Scholarly communication | 0.007 | 0.017 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.008 | 0.013 |
| Insufficient payload (model declined to judge) | 0.013 | 0.003 |
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".