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Record W2159927428 · doi:10.1001/jama.2010.1182

Does This Patient Have Delirium?

2010· review· en· W2159927428 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueJAMA · 2010
Typereview
Languageen
FieldMedicine
TopicIntensive Care Unit Cognitive Disorders
Canadian institutionsUniversity of TorontoSt. Michael's Hospital
Fundersnot available
KeywordsDeliriumMedicineConfusionRating scaleMEDLINEIntensive care medicinePsychiatryEmergency medicinePsychology

Abstract

fetched live from OpenAlex

CONTEXT: Delirium occurs in many hospitalized older patients and has serious consequences including increased risk for death and admission to long-term care. Despite its importance, health care clinicians often fail to recognize delirium. Simple bedside instruments may lead to improved identification. OBJECTIVE: To systematically review the evidence on the accuracy of bedside instruments in diagnosing the presence of delirium in adults. DATA SOURCES: Search of MEDLINE (from 1950 to May 2010), EMBASE (from 1980 to May 2010), and references of retrieved articles to identify studies of delirium among inpatients. STUDY SELECTION: Prospective studies of diagnostic accuracy that compared at least 1 delirium bedside instrument to the Diagnostic and Statistical Manual of Mental Disorders-based diagnosis made by a geriatrician, psychiatrist, or neurologist. DATA SYNTHESIS: There were 6570 unique citations identified with 25 prospectively conducted studies (N = 3027 patients) meeting inclusion criteria and describing use of 11 instruments. Positive results that suggested delirium with likelihood ratios (LRs) greater than 5.0 were present for the Global Attentiveness Rating (GAR), Memorial Delirium Assessment Scale (MDAS), Confusion Assessment Method (CAM), Delirium Rating Scale Revised-98 (DRS-R-98), Clinical Assessment of Confusion (CAC), and Delirium Observation Screening Scale (DOSS). Normal results that decreased the likelihood of delirium with LRs less than 0.2 were calculated for the GAR, MDAS, CAM, DRS-R-98, Delirium Rating Scale (DRS), DOSS, Nursing Delirium Screening Scale (Nu-DESC), and Mini-Mental State Examination (MMSE). The Digit Span test and Vigilance "A" test in isolation have limited utility in diagnosing delirium. Considering the instrument's ease of use, test performance, and clinical importance of the heterogeneity in the confidence intervals (CIs) of the LRs, the CAM has the best available supportive data as a bedside delirium instrument (summary-positive LR, 9.6; 95% CI, 5.8-16.0; summary-negative LR, 0.16; 95% CI, 0.09-0.29). Of all scales, the MMSE (score <24) was the least useful for identifying a patient with delirium (LR, 1.6; 95% CI, 1.2-2.0). CONCLUSION: The choice of instrument may be dictated by the amount of time available and the discipline of the examiner; however, the best evidence supports use of the CAM, which takes 5 minutes to administer.

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.

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)0.012
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Insufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.914
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.012
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0010.001

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 head0.024
GPT teacher head0.317
Teacher spread0.293 · 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