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Record W3171850485 · doi:10.4235/agmr.21.0048

Post-intensive Care Syndrome

2021· article· en· W3171850485 on OpenAlexaboutno aff
Muhammad Hanif Ahmad, Shyh Poh Teo

Bibliographic record

VenueAnnals of Geriatric Medicine and Research · 2021
Typearticle
Languageen
FieldMedicine
TopicIntensive Care Unit Cognitive Disorders
Canadian institutionsnot available
Fundersnot available
KeywordsNeurocognitiveCognitionMedicineIntensive care unitMoodRehabilitationIntensive carePsychiatryIntensive care medicinePhysical therapy

Abstract

fetched live from OpenAlex

The increasing survival rate after discharge from the intensive care unit (ICU) has revealed long-term impairments in the cognitive, psychiatric, and physical domains among survivors. However, clinicians often fail to recognize this post-ICU syndrome (PICS) and its debilitating effects on family members (PICS-F). This study describes two cases of PICS to illustrate the different impairments that may occur in ICU survivors. The PICS risk factors for each domain and the interactions among risk factors are also described. In terms of diagnostic evaluation, limited evidence-based or validated tools are available to assist with screening for PICS. Clinicians should be aware to monitor for its symptoms on the basis of cognitive, psychiatric, and physical domains. The Montreal Cognitive Assessment is recommended to screen for cognition, as it has a high sensitivity and can evaluate executive function. Mood disorders should also be screened. For mobile patients, a 6-minute walk test should be performed. PICS can be prevented by applying the ABCDEF bundle ABCDEF bundle in ICU described in this paper. Finally, the family members of patients in the ICU should be involved in patient care and a tactful communication approach is required to reduce the risk of PICS-F.

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)0.001
metaresearch head score (Gemma)0.035
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.755
Threshold uncertainty score0.973

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.035
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.123
GPT teacher head0.436
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 teacher head, not a consensus.

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

Citations52
Published2021
Admission routes1
Has abstractyes

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