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Record W3135813401 · doi:10.3389/fonc.2021.629207

A Pragmatic Non-Randomized Trial of Prehabilitation Prior to Cancer Surgery: Study Protocol and COVID-19-Related Adaptations

2021· article· en· W3135813401 on OpenAlexaffabout
Daniel Santa Mina, Daniel Sellers, Darren Au, Shabbir M.H. Alibhai, Hance Clarke, Brian H. Cuthbertson, Gail Darling, Alaa El Danab, Anand Govindarajan, Karim S. Ladha, Andrew Matthew, Stuart A. McCluskey, Karen A. Ng, Fayez A. Quereshy, Keyvan Karkouti, Ian Randall

Bibliographic record

VenueFrontiers in Oncology · 2021
Typearticle
Languageen
FieldMedicine
TopicCOVID-19 and healthcare impacts
Canadian institutionsSt. Michael's HospitalPrincess Margaret Cancer CentreSinai Health SystemSunnybrook Health Science CentreUniversity Health NetworkUniversity of TorontoHealth Sciences Centre
Fundersnot available
KeywordsPrehabilitationMedicineRandomized controlled trialClinical trialPhysical therapyIntervention (counseling)NursingSurgeryInternal medicine

Abstract

fetched live from OpenAlex

Background Experimental data highlight the potential benefits and health system cost savings related to surgical prehabilitation; however, adequately powered randomized controlled trial (RCT) data remain nascent. Emerging prehabilitation services may be informed by early RCT data but can be limited in informing real-world program development. Pragmatic trials emphasize external validity and generalizability to understand and advise intervention development and implementation in clinical settings. This paper presents the methodology of a pragmatic prehabilitation trial to complement emerging phase III clinical trials and inform implementation strategies. Methods This is a pilot pragmatic clinical trial conducted in a large academic hospital in Toronto, Ontario, Canada to assess feasibility of clinical implementation and derive estimates of effectiveness. Feasibility data include program referral rates, enrolment and attrition, intervention adherence and safety, participant satisfaction, and barriers and facilitators to programming. The study aims to receive 150 eligible referrals for adult, English-speaking, preoperative oncology patients with an identified indication for prehabilitation ( e.g ., frailty, deconditioning, malnutrition, psychological distress). Study participants undergo a baseline assessment and shared-decision making regarding the intervention setting: either facility-based prehabilitation or home-based prehabilitation. In both scenarios, participants receive an individualized exercise prescription, stress-reduction psychological support, nutrition counseling, and protein supplementation, and if appropriate, smoking cessation program referrals. Secondary objectives include estimating intervention effects at the week prior to surgery and 30 and 90 days postoperatively. Outcomes include surgical complications, postoperative length of stay, mortality, hospital readmissions, physical fitness, psychological well-being, and quality of life. Data from participants who decline the intervention but consent for research-related access to health records will serve as comparators. The COVID-19 pandemic required the introduction of a ‘virtual program’ using only telephone or internet-based communication for screening, assessments, or intervention was introduced. Conclusion This pragmatic trial will provide evidence on the feasibility and viability of prehabilitation services delivered under usual clinical conditions. Study amendments due to the COVID-19 pandemic are presented as strategies to maintain prehabilitation research and services to potentially mitigate the consequences of extended surgery wait times.

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)0.017
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
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 score0.124
Threshold uncertainty score0.997

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.017
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
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 head0.054
GPT teacher head0.466
Teacher spread0.412 · 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 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

Citations20
Published2021
Admission routes2
Has abstractyes

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