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Record W2762502796 · doi:10.1097/sle.0000000000000480

Clinical Indicators of Postoperative Bleeding in Bariatric Surgery

2017· article· en· W2762502796 on OpenAlexaff
Andras B. Fecso, Timothy Samuel, Ahmad Elnahas, Sanjeev Sockalingam, Timothy Jackson, Fayez Quereshy, Allan Okrainec

Bibliographic record

VenueSurgical Laparoscopy Endoscopy & Percutaneous Techniques · 2017
Typearticle
Languageen
FieldMedicine
TopicBariatric Surgery and Outcomes
Canadian institutionsUniversity of TorontoUniversity Health NetworkToronto Western Hospital
Fundersnot available
KeywordsMedicineSurgeryRetrospective cohort studyDemographicsSleeve gastrectomyBlood pressureGastric bypassWeight lossInternal medicineObesity

Abstract

fetched live from OpenAlex

The purpose of this study was to evaluate the relationship between patient, clinical and laboratory characteristics, and bleeding after bariatric surgery. A retrospective chart review was performed for all patients, who underwent a laparoscopic Roux-en-Y gastric bypass and laparoscopic sleeve gastrectomy at our institution between March 2012 and May 2014. In total, 788 patients were included in this study. Seventeen patients had postoperative bleeding. The demographics, comorbidities, and preoperative medications were similar between the groups. Mean postoperative hemoglobin in the bleeding group was significantly lower (94 vs. 126; P<0.001) with a larger decrease from the baseline value (-43 vs. -12; P<0.001). The mean heart rate (91 vs. 81; P<0.001) and its increase from baseline (12 vs. -0.01; P<0.001) were significantly different in the bleeding patients. Postoperative hemoglobin and heart rate were associated with bleeding but not systolic blood pressure or patient characteristics. Further research is needed to develop a robust predictive model.

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)0.002
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.905
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0010.001
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0010.001
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 head0.027
GPT teacher head0.353
Teacher spread0.326 · 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.

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

Citations33
Published2017
Admission routes1
Has abstractyes

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