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Record W1848759700 · doi:10.1089/bari.2014.0034

Is Bariatric Surgery Safe in Patients with Cirrhosis? An Analysis of Short-Term Outcomes

2015· article· en· W1848759700 on OpenAlexaff
Andrew Smith, Ahmad Elnahas, Allan Okrainec, Fayez A. Quereshy, Timothy Jackson

Bibliographic record

VenueBariatric Surgical Practice and Patient Care · 2015
Typearticle
Languageen
FieldMedicine
TopicLiver Disease Diagnosis and Treatment
Canadian institutionsUniversity of TorontoUniversity Health Network
Fundersnot available
KeywordsMedicineCirrhosisAdjustable gastric bandSleeve gastrectomySurgeryAscitesCohortEsophageal varicesRetrospective cohort studyWeight lossLaparoscopyPortal hypertensionGeneral surgeryInternal medicineObesityGastric bypass

Abstract

fetched live from OpenAlex

Introduction: Non-alcoholic fatty liver disease (NAFLD) is an increasingly recognized cause of chronic liver disease with the potential to progress to cirrhosis. Given the association between NAFLD and obesity, bariatric surgeons are increasingly being asked to consider cirrhotic patients as potential surgical candidates. Methods: We conducted a retrospective cohort study using ACS-NSQIP Participant Use File 2005–2011 to identify patients with cirrhosis undergoing elective bariatric procedures. Results: Eleven patients were identified as having a bariatric procedure in the setting of cirrhosis. Eight of the 11 patients (72.7%) had evidence of ascites, while seven (63.6%) had evidence of esophageal varices. Five patients underwent laparoscopic Roux-en-Y gastric bypass, while five underwent laparoscopic adjustable gastric band and one laparoscopic sleeve gastrectomy. The mean length of stay was 2.1 days. One patient had an unexpected return to the operating room, while another patient had a postoperative urinary tract infection. No other postoperative complications or mortalities were identified within 30 days of surgery. Conclusions: Bariatric surgery was performed with minimal 30-day morbidity and no mortality in a select cohort of obese patients with cirrhosis.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation 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 score0.010
Threshold uncertainty score0.698

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
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 head0.032
GPT teacher head0.301
Teacher spread0.268 · 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.

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".

Quick stats

Citations0
Published2015
Admission routes1
Has abstractyes

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