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Modifiable factors associated with weight regain after bariatric surgery: a scoping review

2020· review· en· W2942600835 on OpenAlexafffund
Lisa Kaouk, Amy T. Hsu, Peter Tanuseputro, Mahsa Jessri

Bibliographic record

VenueF1000Research · 2020
Typereview
Languageen
FieldMedicine
TopicBariatric Surgery and Outcomes
Canadian institutionsUniversity of OttawaBruyèreOttawa HospitalMcGill University Health Centre
FundersCanadian Institutes of Health ResearchUniversity of Waterloo
KeywordsMedicineWeight lossObesityGerontologyEnvironmental healthInternal medicine

Abstract

fetched live from OpenAlex

Background: Although bariatric surgery is the most effective treatment for severe obesity, weight regain may still occur. While non-modifiable factors associated with weight regain have been explored, modifiable factors responsible for weight regain are understudied. This scoping review aimed to identify modifiable behaviors associated with weight regain after bariatric surgery. Methods: A systematic search was conducted in Medline, Google Scholar, Cochrane, National Collaborating Centre for Methods and Tools (NCCMT) and Practice-based Evidence in Nutrition (PEN) which included articles published between January 1990 and February 2 2017, for studies examining “weight regain” after bariatric surgery. A total of 293 citations were retrieved. Eligible articles must have examined modifiable factors and addressed weight regain, or a long-term post-operative phase in which weight regain may occur. After removing duplicates, 22 studies were included for thematic analysis. Results: Key modifiable factors associated with weight regain were identified and categorized under the following themes: poor dietary adherence (e.g. excessive calorie, carbohydrate, and alcohol intake), maladaptive eating behaviors (e.g. grazing, binging), lack of on-going follow-up with the bariatric team and insufficient physical activity. Conclusions: Health professionals and self-monitoring tools for patients who have undergone bariatric surgery may benefit from these findings to direct their education and interventions to target behavior change.

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

Direct model labels (unvalidated)

Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.

Model armCategoriesStudy designConfidence
gemmano category
Domain: not available · Genre: Review
About the Canadian research system: no · About a Canadian topic: no
Systematic reviewlow
gptno category
Domain: not available · Genre: Review
About the Canadian research system: no · About a Canadian topic: no
Other designlow
models splitAgreement compares identical category sets and study designs across arms.

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)0.015
metaresearch head score (Gemma)0.081
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: Systematic review
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.015
Threshold uncertainty score0.077

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0150.081
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0050.006
Bibliometrics0.0150.018
Science and technology studies0.0010.001
Scholarly communication0.0040.003
Open science0.0020.002
Research integrity0.0030.002
Insufficient payload (model declined to judge)0.0050.001

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 head0.160
GPT teacher head0.391
Teacher spread0.231 · 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

Labeled directly by 2 models reading the full record.

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

The models disagree on parts of this classification; every voice is preserved in the section at the end of the page.

Study designSystematic review · Other design
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

Citations32
Published2020
Admission routes2
Has abstractyes

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