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Results from a randomised, open-label trial of a multimodal intervention (exercise, nutrition and anti-inflammatory medication) plus standard care versus standard care alone to attenuate cachexia in patients with advanced cancer undergoing chemotherapy.

2024· article· en· W4399451898 on OpenAlexafffund
Tora S. Solheim, Barry Laird, Trude R. Balstad, Guro B. Stene, Vickie E. Baracos, Asta Bye, Olav Dajani, Andrew Eugene Hendifar, Florian Strasser, Martin Chasen, Matthew Maddocks, Melanie Rae Simpson, Eva Skovlund, Gareth Griffiths, Jonathan Hicks, Janet Graham, Fiona Kyle, Joanna Bowden, Marie Fallon, Stein Kaasa

Bibliographic record

VenueJournal of Clinical Oncology · 2024
Typearticle
Languageen
FieldMedicine
TopicNutrition and Health in Aging
Canadian institutionsMcMaster UniversityUniversity of TorontoUniversity of Alberta
FundersRising Tide Foundation for Clinical Cancer ResearchAlberta Cancer Foundation
KeywordsMedicineCachexiaWeight lossRandomized controlled trialCancerPhysical therapyPancreatic cancerClinical endpointInternal medicineMultimodal therapyLung cancerObesity

Abstract

fetched live from OpenAlex

LBA12007 Background: Cancer cachexia arises from the interaction between the host and the tumour, triggering an inflammatory response that leads to weight and appetite loss, diminished physical activity, reduced treatment efficacy and survival. Combining interventions to address inflammation, weight loss, and physical activity is proposed as an effective strategy. Building on a promising pilot study, we conducted the MENAC (Multimodal Exercise Nutrition Anti-inflammatory Cachexia) trial to comprehensively evaluate this approach in patients with lung and pancreatic cancer undergoing systemic anti-cancer treatment (SACT). Methods: MENAC was an investigator-initiated, multicentre, open label, randomised phase 3 trial conducted at 17 sites in 4 countries. Patients with stage III or IV lung or pancreatic cancer receiving SACT with non-curative intent were randomly assigned (1:1) to a multimodal intervention consisting of nutritional counselling plus fish oil containing oral nutritional supplements, physical exercise [endurance and strength] and non-steroidal anti-inflammatory drugs [NSAIDs]) versus standard care. Randomisation was stratified by country, cancer type and stage. Primary Objective: To assess differences between arms in change in body weight. Secondary Objectives: To assess differences in muscle mass (measured by CT L3 technique) and physical activity (assessed through step counts using ActivPAL activity meter) between arms. Assessments were conducted at basline (pre-randomisation) and at endpoint (after 6 weeks). Results: From May 2015 to February 2022, 212 patients were enrolled (105 to multimodal treatment, 107 standard care). Over 6 weeks, weight stabilised in patients assigned to multimodal treatment compared with those assigned to standard care (mean weight change [SD] 0.05 kg [3.8] vs – 0.99 kg [3.2], respectively) with a mean difference in weight change of -1.04, 95 % CI -2.02 to -0.06, p=0.04. There was no conclusive difference in muscle mass (mean change [SD] -6.5cm2 [ 10.1] vs -6.3cm2 [11.9], p=0.93) or in mean step counts [SD] (-377.7 [2075] vs -458 [1858], p=0.89). There were 28 and 24 reported SAEs in the intervention and control arm respectively, no SUSARs were reported. Conclusions: A multimodal cachexia intervention stabilised weight compared to standard care at six weeks. There was no difference in physical activity or muscle mass between trial arms. Clinical trial information: NCT02330926 .

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)0.005
metaresearch head score (Gemma)0.006
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Randomized trial · Consensus signal: Randomized trial
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.015
Threshold uncertainty score0.050

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0050.006
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0050.004
Bibliometrics0.0010.001
Science and technology studies0.0010.002
Scholarly communication0.0010.002
Open science0.0010.001
Research integrity0.0030.004
Insufficient payload (model declined to judge)0.0150.001

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 head0.077
GPT teacher head0.474
Teacher spread0.397 · 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 designRandomized trial
Domainnot available
GenreEmpirical

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

Citations18
Published2024
Admission routes2
Has abstractyes

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