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Record W2922388823 · doi:10.1093/jcag/gwz006.159

A160 CLOSTRIDIUM DIFFICILE AFTER LAPARASCOPIC BARIATRIC SURGERY: AN ANALYSIS OF THE METABOLIC AND BARIATRIC SURGERY ACCREDITATION AND QUALITY IMPROVEMENT PROGRAM

2019· article· en· W2922388823 on OpenAlexaffabout
ThucNhi T. Dang, Jerry T. Dang, Muhammad Moolla, Noah J. Switzer, Karen Madsen, Daniel W. Birch, Shahzeer Karmali

Bibliographic record

VenueJournal of the Canadian Association of Gastroenterology · 2019
Typearticle
Languageen
FieldMedicine
TopicBariatric Surgery and Outcomes
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsMedicineClostridium difficileSleeve gastrectomyIncidence (geometry)Body mass indexSurgeryObesityCohortLogistic regressionOverweightWeight lossGastric bypassInternal medicine

Abstract

fetched live from OpenAlex

Obesity is associated with disturbances in the gut microbiota and reduced microbial diversity, both of which are risk factors for Clostridium difficile infection (CDI). Patients undergoing bariatric surgery incur substantive changes to their gut microbiota which may affect their risk for developing CDI. 1. Assess the risk of developing postoperative CDI within 30 days after laparoscopic Roux-en-Y gastric bypass (LRYGB) and laparoscopic sleeve gastrectomy (LSG) The Metabolic and Bariatric Surgery Accreditation and Quality Improvement Program (MBSAQIP) is a clinically-rich database that captures variables specific to bariatric surgery from 791 centers in the United States and Canada. We identified all patients undergoing LRYGB or LSG in 2016. Patients undergoing revisional bariatric surgery were excluded. Primary outcomes of interest included the prevalence and predictors of CDI after bariatric surgery. A purposeful selection algorithm was used to develop a multivariable logistic regression model to determine preoperative factors predictive of 30-day CDI. A total of 38,737 LRYGB and 106,133 LSG were included. Mean age was 44.6 ± 12.0 years, 79.5% were female and mean body mass index was 45.3 ± 7.8 kg/m2. Mean operative time was 85.1 ± 46.9 minutes. The overall incidence of CDI among patients undergoing LRYGB or LSG was low with only 204 patients (0.14%) developing CDI. However, the incidence of CDI was significantly higher in the LRYGB cohort (0.20 vs 0.12%, p < 0.001). Although incidence was low, CDI was associated with increased major complications including leak (2.5 vs 0.4%, p < 0.001), bleed (3.4 vs 0.9%, p < 0.001), sepsis (2.5 vs 0.1%, p < 0.001), and others (Table 1). Patients with CDI also had significantly higher rates of reoperations (7.8 vs 1.2%, p < 0.001), non-operative reinterventions (10.3 vs 1.3%, p < 0.001), and readmissions (52.5 vs 3.7%, p < 0.001). Multivariable logistic regression determined the following factors that were independently predictive of CDI: LRYGB (OR 1.42, CI 1.04–1.95, p = 0.030), female sex (OR 1.74, CI 1.17–2.59, p = 0.006), white race (OR 1.89, 1.30–2.76, p = 0.001), previous VTE (OR 2.57, CI 1.43–4.64, p = 0.002), smoking (OR 1.65, CI 1.10–2.46, p=0.015), poor functional status OR 2.59, CI 1.09–6.14, p = 0.030), and prolonged operative time (OR 1.21, CI 1.04–1.41, p = 0.016). The overall risk of CDI after bariatric surgery is low, even compared to inpatient surgical patients in which rates have been reported to be as high as 0.5% in other studies. Compared to LSG, LRYGB is associated with an elevated risk of developing CDI within the first 30 days after surgery. This increased risk may be a result of changes in gut microbiota which is seen more in in LRYGB compared to LSG. Table 1. CDI risk by operation, perioperative factors, and 30-day complications None

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)0.002
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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.042
Threshold uncertainty score0.084

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.005
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0020.004
Science and technology studies0.0010.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.012
GPT teacher head0.255
Teacher spread0.243 · 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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Citations0
Published2019
Admission routes2
Has abstractyes

Explore more

Same venueJournal of the Canadian Association of Gastroenterology→Same topicBariatric Surgery and Outcomes→French-language works237,207→