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Record W1760846296 · doi:10.1177/1054773815603346

RADAR

2015· article· en· W1760846296 on OpenAlexafffund
Philippe Voyer, Nathalie Champoux, Johanne Desrosiers, Philippe Landreville, Jane McCusker, Johanne Monette, Maryse Savoie, Pierre‐Hugues Carmichael, Hélène Richard, Sylvie Richard

Bibliographic record

VenueClinical Nursing Research · 2015
Typearticle
Languageen
FieldMedicine
TopicIntensive Care Unit Cognitive Disorders
Canadian institutionsJewish General HospitalMcGill UniversityUniversité de SherbrookeUniversité LavalSt Mary's Hospital CentreCentre for Excellence in Mining Innovation
FundersCanadian Institutes of Health Research
KeywordsVital signsGeneralizability theoryConfusionDeliriumSign (mathematics)MedicineRadarPsychologyIntensive care medicineComputer scienceSurgery

Abstract

fetched live from OpenAlex

The objective of this study was to investigate the potential of RADAR (Recognizing Active Delirium As part of your Routine) as a measure of the sixth vital sign. This study was a secondary analysis of a study (N = 193) that took place in one acute care hospital and one long-term care facility. The primary outcome was a positive sixth vital sign, defined as the presence of both an altered level of consciousness and inattention. These indicators were assessed using the Confusion Assessment Method. RADAR identified 30 of the 43 participants as having a positive sixth vital sign and 58 of the 70 cases as not, yielding a sensitivity and specificity of 70% and 83%, respectively. Positive predictive value was 71%. RADAR's characteristics, including its brevity and acceptability by nursing staff, make this tool a good candidate as a measure of the sixth vital sign. Future studies should address the generalizability of RADAR among various populations and clinical settings.

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)0.003
metaresearch head score (Gemma)0.017
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: Other · Consensus signal: none
Teacher disagreement score0.018
Threshold uncertainty score0.000

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.017
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0180.010

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 head0.357
GPT teacher head0.581
Teacher spread0.224 · 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
GenreOther

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

Citations14
Published2015
Admission routes2
Has abstractyes

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