MétaCan
Menu
Back to cohort
Record W3137506309 · doi:10.1891/wfccn-d-20-00014

Altered Mental Status: An Exploration of Definitions and Descriptors in the Literature

2020· article· en· W3137506309 on OpenAlexaff
Yuko Ikematsu, Elizabeth Papathanassoglou

Bibliographic record

VenueConnect The World of Critical Care Nursing · 2020
Typearticle
Languageen
FieldMedicine
TopicCardiac Arrest and Resuscitation
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsPsychologyCognitive psychology

Abstract

fetched live from OpenAlex

Objectives: To examine the use of “altered mental status” in studies addressing states of shock, by reviewing published English literature. We explored how the term is defined and described in the literature, and alternative words/phrases used. Background: Assessment of mental status is crucial for patients in shock and life-threatening conditions. The term “altered mental status” is being used inconsistently, and varied means of assessment have been reported, which may have implications for critical care nurses' training and implementation of clinical practice guidelines. Methods: A systemized literature review based on targeted searches in CINAHL and MEDLINE, with predefined eligibility criteria. Primary studies, reviews and case studies were included. Results: Based on eligibility criteria, 92 articles were included (48 primary studies, 32 case reports, 12 review articles). Glasgow Coma Scale (GCS) was most frequently used to define “altered mental status” followed by the terms “unconsciousness”, “confusion” “coma” and “disorientation”. Changes in consciousness were described in a variety of expressions, i.e. decreased level of consciousness, change in awareness, and GCS. Conclusion: There is no universal definition for altered mental status. More work is needed towards an accurate definition standardization of use of related terms, and consensus on the most valid assessment methods in order to identify patients with high risk for deterioration.

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: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.142
Threshold uncertainty score0.161

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 communication0.0000.000
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 head0.090
GPT teacher head0.361
Teacher spread0.270 · 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 designQualitative
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
Published2020
Admission routes1
Has abstractyes

Explore more

Same venueConnect The World of Critical Care NursingSame topicCardiac Arrest and ResuscitationFrench-language works237,207