MétaCan
Menu
Back to cohort
Record W3023466710 · doi:10.1097/aco.0000000000000854

Prehabilitation: the anesthesiologist's role and what is the evidence?

2020· review· en· W3023466710 on OpenAlexaff
Enrico Maria Minnella, Miquel Coca-Martínez, Francesco Carli

Bibliographic record

VenueCurrent Opinion in Anaesthesiology · 2020
Typereview
Languageen
FieldMedicine
TopicEnhanced Recovery After Surgery
Canadian institutionsMcGill University Health CentreMcGill UniversityMontreal General Hospital
Fundersnot available
KeywordsPrehabilitationMedicinePhysical therapy

Abstract

fetched live from OpenAlex

PURPOSE OF REVIEW: Surgery poses major threats to functional independence. Prehabilitation is a preoperative conditioning intervention that aims to prevent or attenuate surgery-related functional decline and its consequences. The present review is to summarize most recent evidence on the effectiveness of prehabilitation on key topics in cancer care, such as perioperative functional capacity, surgical and oncologic outcomes. RECENT FINDINGS: Recent studies predominantly focus on functional outcomes, demonstrating a positive effect of prehabilitation on perioperative physical fitness. SUMMARY: Prehabilitation prevents functional decline associated with major cancer surgery. Evidence is still needed to support its effectiveness in relation to postoperative complication, length of hospital stay, tumor progression, response to medical treatment, and survival. Ongoing and future research is essential to prompt the role of perioperative medicine in cancer care.

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 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)0.004
metaresearch head score (Gemma)0.016
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 score0.007
Threshold uncertainty score0.022

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.016
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0040.002
Bibliometrics0.0030.003
Science and technology studies0.0010.001
Scholarly communication0.0030.003
Open science0.0020.001
Research integrity0.0030.002
Insufficient payload (model declined to judge)0.0070.001

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 head0.122
GPT teacher head0.400
Teacher spread0.277 · 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 source (direct Gemma or distilled Codex), not a consensus.

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

Citations17
Published2020
Admission routes1
Has abstractyes

Explore more

Same venueCurrent Opinion in AnaesthesiologySame topicEnhanced Recovery After SurgeryFrench-language works237,207