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Association Between Bariatric Surgery and Major Adverse Diabetes Outcomes in Patients With Diabetes and Obesity

2021· article· en· W3157567365 on OpenAlexafffundabout
Aristithes G. Doumouras, Yung Lee, J. Michael Paterson, Hertzel C. Gerstein, Baiju R. Shah, Branavan Sivapathasundaram, Jean‐Éric Tarride, Mehran Anvari, Dennis Hong

Bibliographic record

VenueJAMA Network Open · 2021
Typearticle
Languageen
FieldMedicine
TopicBariatric Surgery and Outcomes
Canadian institutionsPrograms for Assessment of Technology in Health Research InstituteSt. Joseph’s Healthcare HamiltonInstitute for Clinical Evaluative SciencesSunnybrook HospitalPopulation Health Research InstituteUniversity of TorontoMcMaster University
FundersOntario Ministry of Health and Long-Term Care
KeywordsMedicineBody mass indexType 2 diabetesRetrospective cohort studyObesityPopulationDiabetes mellitusCohort studyMedical recordConfoundingSurgeryInternal medicineEmergency medicinePediatricsEnvironmental health

Abstract

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Importance: There are high-quality randomized clinical trial data demonstrating the effect of bariatric surgery on type 2 diabetes remission, but these studies are not powered to study mortality in this patient group. Large observational studies are warranted to study the association of bariatric surgery with mortality in patients with type 2 diabetes. Objective: To determine the association between bariatric surgery and all-cause mortality among patients with type 2 diabetes and severe obesity. Design, Setting, and Participants: This retrospective, population-based matched cohort study included patients with type 2 diabetes and body mass index (BMI; calculated as weight in kilograms divided by height in meters squared) 35 or greater who underwent bariatric surgery from January 2010 to December 2016 in Ontario, Canada. Multiple linked administrative databases were used to define confounders, including age, baseline BMI, sex, comorbidities, duration of diabetes diagnosis, health care utilization, socioeconomic status, smoking status, substance abuse, cancer screening, and psychiatric history. Potential controls were identified from a primary care electronic medical record database. Data were analyzed in 2020. Exposure: Bariatric surgery (gastric bypass and sleeve gastrectomy) and nonsurgical management of obesity provided by the primary care physician. Main Outcomes and Measures: The primary outcome was all-cause mortality. Secondary outcomes were cause-specific mortality and nonfatal morbidities. Groups were compared through a multivariable Cox proportional Hazards model. Results: A total of 6910 patients (mean [SD] age at baseline, 52.04 [9.45] years; 4950 [71.6%] women) were included, with 3455 patients who underwent bariatric surgery and 3455 match controls and a median (interquartile range) follow-up time of 4.6 (3.22-6.35) years. In the surgery group, 83 patients (2.4%) died, compared with 178 individuals (5.2%) in the control group (hazard ratio [HR] 0.53 [95% CI, 0.41-0.69]; P < .001). Bariatric surgery was associated with a 68% lower cardiovascular mortality (HR, 0.32 [95% CI, 0.15-0.66]; P = .002) and a 34% lower rate of composite cardiac events (HR, 0.68 [95% CI, 0.55-0.85]; P < .001). Risk of nonfatal renal events was also 42% lower in the surgical group compared with the control group (HR, 0.58 [95% CI, 0.35-0.95], P = .03). Of the groups that had the highest absolute benefit associated with bariatric surgery, men had an absolute risk reduction (ARR) of 3.7% (95% CI, 1.7%-5.7%), individuals with more than 15 years of diabetes had an ARR of 4.3% (95% CI, 0.8%-7.8%), and individuals aged 55 years or older had an ARR of 4.7% (95% CI, 3.0%-6.4%). Conclusions and Relevance: These findings suggest that bariatric surgery was associated with reduced all-cause mortality and diabetes-specific cardiac and renal outcomes in patients with type 2 diabetes and severe obesity.

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)0.001
metaresearch head score (Gemma)0.003
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.040
Threshold uncertainty score0.079

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.010
GPT teacher head0.224
Teacher spread0.214 · 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 designObservational
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".

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Citations52
Published2021
Admission routes3
Has abstractyes

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