Student or educator? Sign up with your school email and get 6 months of Pro free.
Cited by 507

The DOI matches its Crossref record; 40 of the 40 references it cites were found in a registry or an index.

3D-CAM: Derivation and Validation of a 3-Minute Diagnostic Interview for CAM-Defined Delirium: A Cross-sectional Diagnostic Test Study

Open access
Marcantonio, Edward R., Ngo, Long H., O'Connor, Margaret, Jones, Richard N., Crane, Paul K., Metzger, Eran D., Inouye, Sharon K.·2014·Annals of Internal Medicine

Abstract

BACKGROUND: Delirium is common, leads to other adverse outcomes, and is costly. However, it often remains unrecognized in most clinical settings. The Confusion Assessment Method (CAM) is the most widely used diagnostic algorithm, and operationalizing its features would be a substantial advance for clinical care. OBJECTIVE: To derive the 3D-CAM, a new 3-minute diagnostic assessment for CAM-defined delirium, and validate it against a clinical reference standard. DESIGN: Derivation and validation study. SETTING: 4 general medicine units in an academic medical center. PARTICIPANTS: 201 inpatients aged 75 years or older. MEASUREMENTS: 20 items that best operationalized the 4 CAM diagnostic features were identified to create the 3D-CAM. For prospective validation, 3D-CAM assessments were administered by trained research assistants. Clinicians independently did an extensive assessment, including patient and family interviews and medical record reviews. These data were considered by an expert panel to determine the presence or absence of delirium and dementia (reference standard). The 3D-CAM delirium diagnosis was compared with the reference standard in all patients and subgroups with and without dementia. RESULTS: The 201 participants in the prospective validation study had a mean age of 84 years, and 28% had dementia. The expert panel identified 21% with delirium, 88% of whom had hypoactive or normal psychomotor features. Median administration time for the 3D-CAM was 3 minutes (interquartile range, 2 to 5 minutes), sensitivity was 95% (95% CI, 84% to 99%), and specificity was 94% (CI, 90% to 97%). The 3D-CAM did well in patients with dementia (sensitivity, 96% [CI, 82% to 100%]; specificity, 86% [CI, 67% to 96%]) and without dementia (sensitivity, 93% [CI, 66% to 100%]; specificity, 96% [CI, 91% to 99%]). LIMITATION: Limited to single-center, cross-sectional, and medical patients only. CONCLUSION: The 3D-CAM operationalizes the CAM algorithm using a 3-minute structured assessment with high sensitivity and specificity relative to a reference standard and could be an important tool for improving recognition of delirium. PRIMARY FUNDING SOURCE: National Institute on Aging.

Overview

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Integrity signals

Registry

Crossref · 10.7326/m14-0865

Cites

40 of 40 references verified0 citation errors · 0 not checkable

Statistics

Verification

Claims the paper makes

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Self-check

Percentages, sums and reported differences re-computed from the paper’s own numbers.

The self-check needs the paper’s full text.

Citations

Cites

Show all 40 references

Cited by

See all 507

Metadata

Authors
Marcantonio, E. R., Ngo, L. H., O'Connor, M., Jones, R. N., Crane, P. K., Metzger, E. D., & Inouye, S. K.
Venue
Annals of Internal Medicine
Published
2014-10-21
Volume
161
Issue
8
Pages
554-561
ISSN
  • 0003-4819 (ISSN-L)
  • 1539-3704
OpenAlex
W1973943844
Cited by
507
References
40
FWCI
11.54
Type
journal article
Language
en
Open access
green

About these checks

Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.

Statistics and arithmetic are recomputed, not judged.

Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.

What counts as a claim ›How we measure confidence ›How we check this ›

Are you an author, or do you see a problem with this page? Request a review

How to cite

Cite CoChat for the checks on this page, and the paper for its own findings.

This page
CoChat. (2026). Citation check: 3D-CAM: Derivation and Validation of a 3-Minute Diagnostic Interview for CAM-Defined Delirium: A Cross-sectional Diagnostic Test Study. https://app.cochat.ai/papers/10.7326/m14-0865
The paper
Marcantonio, E. R., Ngo, L. H., O'Connor, M., Jones, R. N., Crane, P. K., Metzger, E. D., & Inouye, S. K. (2014). 3D-CAM: Derivation and Validation of a 3-Minute Diagnostic Interview for CAM-Defined Delirium: A Cross-sectional Diagnostic Test Study. Annals of Internal Medicine. https://doi.org/10.7326/m14-0865

Data sources

Bibliographic data: Semantic Scholar, OpenAlex, Crossref.

Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).

Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.