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Record W2057112217 · doi:10.1186/1472-6963-13-321

The evaluating self-management and educational support in severely obese patients awaiting multidisciplinary bariatric care (EVOLUTION) trial: rationale and design

2013· article· en· W2057112217 on OpenAlexafffundabout
Raj Padwal, Arya M. Sharma, Miriam Fradette, Susan Jelinski, Scott Klarenbach, Alun Edwards, Sumit R. Majumdar

Bibliographic record

VenueBMC Health Services Research · 2013
Typearticle
Languageen
FieldMedicine
TopicBariatric Surgery and Outcomes
Canadian institutionsAlberta Health ServicesDiabetes CanadaUniversity of CalgaryUniversity of Alberta
FundersCanadian Institutes of Health Research
KeywordsMedicineWeight lossMultidisciplinary approachIntervention (counseling)Weight managementRandomized controlled trialClinical trialPhysical therapyHealth informaticsNursing researchHealth carePublic healthFamily medicineObesityNursingSurgeryInternal medicine

Abstract

fetched live from OpenAlex

BACKGROUND: In Canada, demand for multidisciplinary bariatric (obesity) care far outstrips capacity. Consequently, prolonged wait times exist and contribute to substantial health impairments.A supportive, educational intervention (with in-person and web-based versions) designed to enhance the self-management skills of patients wait-listed for multidisciplinary bariatric medical and surgical care has been variably implemented across Alberta, Canada. However, its effectiveness has not been evaluated. Our objectives were: 1. To determine if this program improves clinical and humanistic outcomes and is cost-effective compared to a control intervention; and 2. To compare the effectiveness and cost-effectiveness of in-person group-based versus web-based care. We hypothesize that both the web-based and in-person programs will reduce body weight and improve outcomes compared to the control group. Furthermore, we hypothesize that the in-person version will be more effective but more costly than the web-based version. METHODS/DESIGN: This pragmatic, prospective controlled trial will enrol 660 wait-listed subjects (220 per study arm) from regional bariatric programs in Alberta and randomly assign them to: 1. an in-person, group-based intervention (9 modules delivered over 10 sessions); 2. a web-based intervention (13 modules); and 3. controls who will receive mailed literature. Subjects will have three months to review the content assigned to them (the intervention period) after which they will immediately enter the weight management clinic. Data will be collected at baseline and every 3 months for 9 months (study end), including: 1. Clinical [5% weight loss responders (primary outcome), absolute and % weight losses, changes in obesity-related comorbidities]; 2. Humanistic (health related quality of life, patient satisfaction, depression, and self-efficacy); and 3. Economic (incremental costs and utilities and cost per change in BMI assessed from the third party health care payor perspective) outcomes. Covariate-adjusted baseline-to-nine-month change-scores will be compared between groups for each outcome using linear regression for continuous outcomes and logistic regression for dichotomous ones. DISCUSSION: Our findings will determine whether this intervention is effective and cost-effective compared to controls and if online or in-person care delivery is preferred. This information will be useful for clinicians, health-service providers and policy makers and should be generalizable to similar publically-funded bariatric care programs. TRIAL REGISTRATION: Trial Identifier: NCT01860131.

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)0.005
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Protocol · Consensus signal: none
Teacher disagreement score0.005
Threshold uncertainty score0.027

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0050.005
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0030.002
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0050.000

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 head0.067
GPT teacher head0.411
Teacher spread0.344 · 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 designNot applicable
Domainnot available
GenreProtocol

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

Citations10
Published2013
Admission routes3
Has abstractyes

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