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Record W6920425756 · doi:10.60692/tx5nm-2mm05

International Analgesia, Sedation, and Delirium Practices: a prospective cohort study

2019· article· en· W6920425756 on OpenAlexaboutno aff

Bibliographic record

VenueGreater South Information System · 2019
Typearticle
Languageen
FieldEngineering
TopicMachine Fault Diagnosis Techniques
Canadian institutionsnot available
Fundersnot available
KeywordsDeliriumSedationProspective cohort studySedativeBenzodiazepineRetrospective cohort study

Abstract

fetched live from OpenAlex

While understanding of critical illness and delirium continue to evolve, the impact on clinical practice is often unknown and delayed. Our purpose was to provide insight into practice changes by characterizing analgesia and sedation usage and occurrence of delirium in different years and international regions.We performed a retrospective analysis of two multicenter, international, prospective cohort studies. Mechanically ventilated adults were followed for up to 28 days in 2010 and 2016. Proportion of days utilizing sedation, analgesia, and performance of a spontaneous awakening trial (SAT), and occurrence of delirium were described for each year and region and compared between years.A total of 14,281 patients from 6 international regions were analyzed. Proportion of days utilizing analgesia and sedation increased from 2010 to 2016 (p < 0.001 for each). Benzodiazepine use decreased in every region but remained the most common sedative in Africa, Asia, and Latin America. Performance of SATs increased overall, driven mostly by the US/Canada region (24 to 35% of days with sedation, p < 0.001). Any delirium during admission increased from 7 to 8% of patients overall and doubled in the US/Canada region (17 to 36%, p < 0.001).Analgesia and sedation practices varied widely across international regions and significantly changed over time. Opportunities for improvement in care include increasing delirium monitoring, performing SATs, and decreasing use of sedation, particularly benzodiazepines.

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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.040
Threshold uncertainty score0.541

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 head0.013
GPT teacher head0.248
Teacher spread0.235 · 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 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

Citations0
Published2019
Admission routes1
Has abstractyes

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