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Record W1995731258 · doi:10.1097/mcc.0b013e32834f186d

Long-term outcome after acute lung injury

2011· review· en· W1995731258 on OpenAlexaff
Catherine L. Hough, Margaret S. Herridge

Bibliographic record

VenueCurrent Opinion in Critical Care · 2011
Typereview
Languageen
FieldMedicine
TopicIntensive Care Unit Cognitive Disorders
Canadian institutionsToronto General HospitalUniversity of Toronto
Fundersnot available
KeywordsMedicinePsychological interventionCognitionDepression (economics)Quality of life (healthcare)Intensive care medicineRandomized controlled trialPsychiatryNursing

Abstract

fetched live from OpenAlex

PURPOSE OF REVIEW: As incidence of acute lung injury (ALI) increases and case fatality decreases, long-term care of survivors is of public health importance. Previous studies demonstrate that these survivors are at risk for impairment in physical, cognitive and mental health. In this review, we will discuss recent studies that add to our knowledge of long-term outcomes after ALI and critical illness. RECENT FINDINGS: New studies show that persisting impairment in physical and cognitive function continues 5 years after recovery from critical illness. Glucose dysregulation may play a role in development of both depression and cognitive impairment. Premorbid impairment appears to be an important risk factor, but critical illness is an independent risk factor of physical and cognitive functional decline. Recent randomized controlled trials emphasize that post-ICU interventions may not be enough to improve health-related quality of life after ALI. Interventions delivered early in critical illness, such as physical and occupational therapy and creation of ICU diaries, may be key in improving late outcomes after ALI. SUMMARY: Physical, cognitive and mental health impairments after ALI are common, persistent and expensive. Future research is needed to improve prediction, prevention and treatment of these important sequelae.

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

Direct model labels (unvalidated)

Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.

Model armCategoriesStudy designConfidence
gptno category
Domain: not available · Genre: Review
About the Canadian research system: no · About a Canadian topic: no
Other designhigh
grokno category
Domain: not available · Genre: Review
About the Canadian research system: no · About a Canadian topic: no
Not applicablehigh
opusno category
Domain: not available · Genre: Review
About the Canadian research system: no · About a Canadian topic: no
Other designhigh
models splitAgreement compares identical category sets and study designs across arms.

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)0.001
metaresearch head score (Gemma)0.004
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Systematic review · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.004
Threshold uncertainty score0.000

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.004
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0030.001
Bibliometrics0.0020.003
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0040.001

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 head0.183
GPT teacher head0.502
Teacher spread0.319 · 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

Labeled directly by 3 models reading the full record.

The models applied no category: nothing in the taxonomy fit this work.

The models disagree on parts of this classification; every voice is preserved in the section at the end of the page.

Study designOther design · Not applicable
Domainnot available
GenreReview

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

Citations53
Published2011
Admission routes1
Has abstractyes

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