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Record W4403329944 · doi:10.1016/j.ejso.2024.108743

A scoping review of preoperative weight loss interventions on postoperative outcomes for patients with gastrointestinal cancer

2024· review· en· W4403329944 on OpenAlexaff
Y J Zhang, Natália Tomborelli Bellafronte, Gezal Najafitirehshabankareh, Michelle Huamani Jimenez, Emily Jaeger-McEnroe, Hughes Plourde, Mary Hendrickson, Chelsia Gillis

Bibliographic record

VenueEuropean Journal of Surgical Oncology · 2024
Typereview
Languageen
FieldMedicine
TopicBariatric Surgery and Outcomes
Canadian institutionsMcGill University
FundersAbbott Nutrition
KeywordsGastrointestinal cancerMedicinePsychological interventionWeight lossCancerGeneral surgeryIntensive care medicineInternal medicineColorectal cancerObesityNursing

Abstract

fetched live from OpenAlex

BACKGROUND: Obesity is associated with increased risk of surgical complications in some settings. OBJECTIVE: As a precursor to a systematic review, we conducted a scoping review of intentional preoperative weight loss to describe these interventions, their feasibility and effectiveness for patients with gastrointestinal cancer. METHODS: In April 2024, Ovid MEDLINE, EMBASE, CINAHL, and Google Scholar were searched for primary studies of intentional weight loss before elective gastrointestinal cancer surgery. Extracted data encompassed recruitment and attrition, intervention types, adherence, anthropometric and body composition changes, and surgical outcomes. Study quality was assessed using the Risk of Bias In Non-randomized Studies of Interventions tool. RESULTS: . Weight loss interventions included dietary modification (n = 3), exercise (n = 1), and combination (n = 3). None of the articles reported rates of recruitment, 2 adherence (97-100 %), and 4 reported attrition rates (0-18 %). All reported weight reductions of -1.3 to -6 kg and 4.5-6.9 % (n = 7), compared to baseline. Three of four non-randomized trials observed a reduction in postoperative complications, as compared to control; yet all trials were at critical risk of bias. CONCLUSION: Strong conclusions could not be made due to the limited reporting and critical risk of bias; further systematic review is not recommended at this time. To establish more robust evidence, there is a clear need for high-quality trials.

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)0.002
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation 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 score0.571
Threshold uncertainty score0.904

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0040.002
Bibliometrics0.0000.000
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 head0.079
GPT teacher head0.420
Teacher spread0.341 · 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.

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

Citations3
Published2024
Admission routes1
Has abstractyes

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