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Record W3025845113 · doi:10.1097/mco.0000000000000663

Prehabilitation: metabolic considerations

2020· review· en· W3025845113 on OpenAlexaff
Katherine Chabot, Chelsia Gillis, Franco Carli

Bibliographic record

VenueCurrent Opinion in Clinical Nutrition & Metabolic Care · 2020
Typereview
Languageen
FieldMedicine
TopicEnhanced Recovery After Surgery
Canadian institutionsMontreal General HospitalMcGill University Health CentreUniversity of CalgaryMcGill University
Fundersnot available
KeywordsPrehabilitationPerioperativeMedicineSarcopeniaInsulin resistanceIntensive care medicinePsychological interventionMetabolic syndromeProtein catabolismInsulinBioinformaticsInternal medicineSurgeryPhysical therapyObesityBiology

Abstract

fetched live from OpenAlex

PURPOSE OF REVIEW: The major components of ERAS attenuate the inflammatory response and modulate metabolism in direction of sparing body protein and preserving function. However, these perioperative interventions might have limited effectiveness on postoperative outcomes if preoperative risk factors are not addressed and optimized. RECENT FINDINGS: The preoperative metabolic perturbations characterized by insulin resistance and sarcopenia might predispose patients to a higher degree of postoperative catabolism. High-risk populations for such metabolic disturbances include elderly and frail patients, and patients with metabolic syndrome. Research on the effect of prehabilitation on perioperative metabolism is limited, but recent findings suggest that interventions designed to improve insulin sensitivity prior to surgery might represent a promising therapeutic target to minimize surgical complications. SUMMARY: The present paper will discuss the metabolic implications of modulating preoperative risk factors with elements of multimodal prehabilitation, such as exercise training and nutrition.

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)0.004
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Research integrity
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.940
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.004
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0060.003
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0010.002
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 head0.214
GPT teacher head0.496
Teacher spread0.281 · 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 designNot 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

Citations15
Published2020
Admission routes1
Has abstractyes

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