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Record W2767905948 · doi:10.1007/s40140-017-0245-2

Prehabilitation and Nutritional Support to Improve Perioperative Outcomes

2017· review· en· W2767905948 on OpenAlexfundno aff
Malcolm West, Paul E. Wischmeyer, Michael P. W. Grocott

Bibliographic record

VenueCurrent anesthesiology reports · 2017
Typereview
Languageen
FieldMedicine
TopicNutrition and Health in Aging
Canadian institutionsnot available
FundersNational Institutes of HealthRoyal College of AnaesthetistsNational Heart, Lung, and Blood InstituteUniversity of SouthamptonCanadian Institutes of Health ResearchNational Institute for Health and Care ResearchEli Lilly and CompanyEdwards LifesciencesNational Institute of Academic AnaesthesiaRoyal SocietyAbbott Laboratories
KeywordsPrehabilitationMedicinePsychological interventionAnesthesiologyPerioperativeIntensive care medicinePhysical therapyElective surgeryQuality of life (healthcare)Clinical nutritionPain medicineSurgeryNursingInternal medicineAnesthesia

Abstract

fetched live from OpenAlex

PURPOSE OF REVIEW: The purpose of this study is to evaluate the role of physical exercise and nutrition interventions in adult patients before elective major surgery. RECENT FINDINGS: Exercise training before elective adult major surgery is feasible, safe, and efficacious, but the clinical effectiveness remains uncertain. Early data suggests a reduction in morbidity, length of stay, and quality of life, but the results of larger definitive studies are awaited. Nutritional interventions are less well evaluated and when they are, it is often in combination with exercise interventions as part of a prehabilitation package. SUMMARY: Studies evaluating exercise and nutrition interventions before elective major surgery in adults are producing encouraging early results, but definitive clinical evidence is currently very limited. Future research should focus on refining interventions, exploring mechanism, and evaluating the interactions between therapies and large-scale clinical effectiveness studies.

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)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.965
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0020.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.179
GPT teacher head0.491
Teacher spread0.312 · 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

Citations165
Published2017
Admission routes1
Has abstractyes

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