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Record W2776741259 · doi:10.1177/1471301217746769

Use of antipsychotics and benzodiazepines for dementia: Time for action? What will be required before global de-prescribing?

2017· article· en· W2776741259 on OpenAlexaboutno aff
Stephen J. Ralph, Anthony Espinet

Bibliographic record

VenueDementia · 2017
Typearticle
Languageen
FieldPsychology
TopicHealthcare Decision-Making and Restraints
Canadian institutionsnot available
Fundersnot available
KeywordsDementiaContext (archaeology)PsychiatryAntipsychoticMedicineAction (physics)Government (linguistics)PsychologyDiseaseSchizophrenia (object-oriented programming)

Abstract

fetched live from OpenAlex

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 .

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.809
Threshold uncertainty score0.706

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.132
GPT teacher head0.426
Teacher spread0.294 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designOther design
Domainnot available
GenreEmpirical

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".

Quick stats

Citations29
Published2017
Admission routes1
Has abstractyes

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