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Record W2138034792 · doi:10.1017/s1742646406000148

Treatment of acute behavioural disturbance: a UK national survey of rapid tranquillisation

2005· article· en· W2138034792 on OpenAlexaff
Stephen Pereira, Carol Paton, Lucy M Walkert, Susan Shaw, Richard Gray, Hiram Joseph Wildgust

Bibliographic record

VenueJournal of Psychiatric Intensive Care · 2005
Typearticle
Languageen
FieldMedicine
TopicSchizophrenia research and treatment
Canadian institutionsInstitute of Health Services and Policy Research
FundersEli Lilly and Company
KeywordsDroperidolMedicineIntervention (counseling)NiceDisturbance (geology)PsychiatryFamily medicineAnesthesia

Abstract

fetched live from OpenAlex

Rapid Tranquillisation (RT), defined as “the use of psychotropic medication to control agitated, threatening or destructive psychotic behaviour”, is a last resort pharmacological intervention. The withdrawal of the widely used droperidol in 2001 due to concerns over QTc prolongation may have increased the variability in RT practice across the UK. This paper reports on a UK wide survey of the practice of RT by psychiatrists. The survey pre-dated publication of NICE guidance on management of distributed behaviour. 257 questionnaires were received (response rate 22%). In comparison with previous surveys of this type, the overall quality of prescribing and monitoring has improved, although some apparently idiosyncratic practice remains. The majority of psychiatrists expressed concerns about prescribing and monitoring RT and would welcome an easy to use algorithm and associated training package. Findings are discussed in terms of training implications and BNF recommendations for high dose prescribing.

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 imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.005
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.111
Threshold uncertainty score0.221

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.005
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.001

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.

Opus teacher head0.049
GPT teacher head0.348
Teacher spread0.299 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
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

Citations10
Published2005
Admission routes1
Has abstractyes

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