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Record W2995603002 · doi:10.1136/bmjopen-2019-031895

Safety and efficacy of perioperative benzodiazepine administration: study protocol for a systematic review and meta-analysis

2019· review· en· W2995603002 on OpenAlexaff
Jessica Spence, Jack Young, Waleed Alhazzani, Richard Whitlock, Frédérick D’Aragon, Kevin J. Um, C. David Mazer, Chris Beaver, Eric Jacobsohn, Emilie P. Belley‐Côté

Bibliographic record

VenueBMJ Open · 2019
Typereview
Languageen
FieldMedicine
TopicIntensive Care Unit Cognitive Disorders
Canadian institutionsUniversity of ManitobaSheridan CollegeUniversity of TorontoSt. Michael's HospitalMcMaster UniversityUniversité de SherbrookeImpactPopulation Health Research Institute
Fundersnot available
KeywordsMedicineObservational studyDeliriumPerioperativeMEDLINECINAHLRandomized controlled trialMeta-analysisBenzodiazepineIntensive care medicineIntensive care unitAdverse effectEmergency medicineAnesthesiaPsychiatryInternal medicinePsychological intervention

Abstract

fetched live from OpenAlex

INTRODUCTION: Perioperative benzodiazepines are used because of their anxiolytic, sedative and amnestic effects. Evidence has demonstrated an association of benzodiazepines with adverse neuropsychiatric effects. Nonetheless, because of their potential benefits, perioperative benzodiazepines continue to be used routinely. We seek to evaluate the body of evidence of the risks and benefits of benzodiazepine use during the perioperative period. METHODS AND ANALYSIS: We will search Cochrane CENTRAL, MEDLINE, EMBASE, PsychINFO, CINAHL and Web of Science from inception to March 2019 for randomised controlled trials (RCTs) and observational studies evaluating the administration of benzodiazepine medications as compared with all other medications (or nothing) in patients undergoing cardiac and non-cardiac surgery. We will exclude studies assessing the use of benzodiazepines for procedural sedation or day surgery. We will examine the impact of giving these medications before, during and after surgery. Outcomes of interest include the incidence of delirium, duration of delirium, postprocedure cognitive change, the incidence of intraoperative awareness, patient satisfaction/quality of life/quality of recovery, length-of-stay (LOS) in the intensive care unit (ICU), hospital LOS and in-hospital mortality.Reviewers will screen references and assess eligibility using predefined criteria independently and in duplicate. Two reviewers will independently collect data using prepiloted forms. We will present results separately for RCTs and observational studies. We will pool data using a random effect model and present results as relative risk with 95% CIs for dichotomous outcomes and mean difference with 95% CI for continuous outcomes. We will pool adjusted ORs for observational studies. We will assess risk of bias for individual studies using the Cochrane Collaboration tool for RCTs. For observational studies, we will use tools designed by the Clinical Advances through Research and Information Translation group. Quality of evidence for each outcome will be assessed using the Grading of Recommendations Assessment, Development and Evaluation approach. ETHICS AND DISSEMINATION: This systematic review involves no patient contact and no interaction with healthcare providers or systems. As such, we did not seek ethics board approval. We will disseminate the findings of our systematic review through the presentation at peer-reviewed conferences and by seeking publication in a peer-reviewed journal. PROSPERO REGISTRATION NUMBER: CRD42019128144.

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)0.033
metaresearch head score (Gemma)0.061
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Protocol · Consensus signal: Protocol
Teacher disagreement score0.058
Threshold uncertainty score0.194

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0330.061
Meta-epidemiology (narrow)0.0050.004
Meta-epidemiology (broad)0.0250.028
Bibliometrics0.0090.010
Science and technology studies0.0020.002
Scholarly communication0.0060.006
Open science0.0040.003
Research integrity0.0040.005
Insufficient payload (model declined to judge)0.0580.004

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 head0.302
GPT teacher head0.548
Teacher spread0.246 · 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 designNot applicable
Domainnot available
GenreProtocol

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

Citations9
Published2019
Admission routes1
Has abstractyes

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