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Record W4323520253 · doi:10.56392/001c.56675

Assessing Recovery from Delirium: An International Survey of Healthcare Professionals Involved in Delirium Care.

2022· article· en· W4323520253 on OpenAlexaboutno aff
Erin Noble, Haruno McCartney, Alasdair M. J. MacLullich, Susan D. Shenkin, Graciela Muñiz‐Terrera, Jonathan J. Evans, Daniel Davis, Daisy Sandeman, Zoë Tieges

Bibliographic record

VenueDelirium · 2022
Typearticle
Languageen
FieldMedicine
TopicIntensive Care Unit Cognitive Disorders
Canadian institutionsnot available
Fundersnot available
KeywordsDeliriumMedicineConfusionHealth careGeriatricsPsychiatryPsychology

Abstract

fetched live from OpenAlex

Background: A crucial part of delirium care is determining if the delirium episode has resolved. Yet, there is no clear evidence or consensus on which assessments clinicians should use to assess for delirium recovery. Objective: To evaluate current opinions from delirium specialists on assessment of delirium recovery. Design: Online questionnaire-based survey distributed internationally to healthcare professionals involved in delirium care. Methods: The survey covered methods for assessing recovery, the importance of different symptom domains for capturing recovery, and local guidance or pathways that recommend monitoring for delirium recovery. Results: Responses from 199 clinicians were collected. Respondents were from the UK (51%), US (13%), Australia (9%), Canada (7%), Ireland (7%) and 16 other countries. Most respondents were doctors (52%) and nurses (27%). Clinicians worked mostly in geriatrics (52%), ICUs (21%) and acute assessment units (17%). Ninety-four percent of respondents indicated that they conduct repeat delirium assessments (i.e., on ≥2 occasions) to monitor delirium recovery. The symptom domains considered most important for capturing recovery were: arousal (92%), inattention (84%), motor disturbance (84%), and hallucinations and delusions (83%). The most used tool for assessing recovery was the 4 'A's Test (4AT, 51%), followed by the Confusion Assessment Method (CAM, 26%), the CAM for the ICU (CAM-ICU, 17%) and the Single Question in Delirium (SQiD, 11%). Twenty-eight percent used clinical features only. Less than half (45%) of clinicians reported having local guidance that recommends monitoring for delirium recovery. Conclusions: The survey results suggest a lack of standardisation regarding tools and methods used for repeat delirium assessment, despite consensus surrounding the key domains for capturing delirium recovery. These findings emphasise the need for further research to establish best practice for assessing delirium recovery.

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)0.009
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.009
Threshold uncertainty score0.017

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.009
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.061
GPT teacher head0.374
Teacher spread0.313 · 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 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

Citations3
Published2022
Admission routes1
Has abstractyes

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