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Record W3036695466 · doi:10.1097/mcc.0000000000000748

Novel approaches to metabolic assessment and structured exercise to promote recovery in ICU survivors

2020· review· en· W3036695466 on OpenAlexfundno aff
Jeroen Molinger, Amy M. Pastva, John Whittle, Paul E. Wischmeyer

Bibliographic record

VenueCurrent Opinion in Critical Care · 2020
Typereview
Languageen
FieldMedicine
TopicIntensive Care Unit Cognitive Disorders
Canadian institutionsnot available
FundersNational Heart, Lung, and Blood InstituteNational Institute on AgingCanadian Institutes of Health Research
KeywordsMedicineRehabilitationIntensive care medicineQuality of life (healthcare)Survivorship curvePhysical medicine and rehabilitationPhysical therapyAthletesNursing

Abstract

fetched live from OpenAlex

PURPOSE OF REVIEW: Survivorship or addressing impaired quality of life (QoL) in ICU survivors has been named 'the defining challenge of critical care' for this century to address this challenge; in addition to optimal nutrition, we must learn to employ targeted metabolic/muscle assessment techniques and utilize structured, progressive ICU rehabilitative strategies. RECENT FINDINGS: Objective measurement tools such as ccardiopulmonary exercise testing (CPET) and muscle-specific ultrasound show great promise to assess/treat post-ICU physical dysfunction. CPET is showing that systemic mitochondrial dysfunction may underlie development and persistence of poor post-ICU functional recovery. Finally, recent data indicate that we are poor at delivering effective, early ICU rehabilitation and that there is limited benefit of currently employed later ICU rehabilitation on ICU-acquired weakness and QoL outcomes. SUMMARY: The combination of nutrition with effective, early rehabilitation is highly likely to be essential to optimize muscle mass/strength and physical function in ICU survivors. Currently, technologies such as muscle-specific ultrasound and CPET testing show great promise to guide ICU muscle/functional recovery. Further, we must evolve improved ICU-rehabilitation strategies, as current methods are not consistently improving outcomes. In conclusion, we must continue to look to other areas of medicine and to athletes if we hope to ultimately improve 'ICU Survivorship'.

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)0.007
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.944
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.007
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0030.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.327
GPT teacher head0.468
Teacher spread0.141 · 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 designOther design
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

Citations21
Published2020
Admission routes1
Has abstractyes

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