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Record W3037666509 · doi:10.1136/jclinpath-2020-206428

Review of pathological findings in laparoscopic sleeve gastrectomy specimens performed for morbid obesity

2020· review· en· W3037666509 on OpenAlexaff
Klaudia Nowak, Adam DiPalma, Stefano Serra, Fayez A. Quereshy, Timothy Jackson, Allan Okrainec, Runjan Chetty

Bibliographic record

VenueJournal of Clinical Pathology · 2020
Typereview
Languageen
FieldMedicine
TopicBariatric Surgery and Outcomes
Canadian institutionsUniversity of TorontoUniversity Health Network
Fundersnot available
KeywordsPathologicalMedicineSleeve gastrectomyHelicobacter pyloriMorbid obesityGastrectomySurgeryGeneral surgeryClinical significanceObesityWeight lossRadiologyPathologyInternal medicineGastric bypassCancer

Abstract

fetched live from OpenAlex

Background Bariatric surgical procedures are employed when there is a failure of lifestyle modification in arresting obesity. Laparoscopic sleeve gastrectomy (LSG) is quickly becoming the bariatric surgical procedure of choice. LSG results in a gastric remnant that is subject to pathological examination. The objective of this paper is to review the literature in regard to histological findings identified in gastric remnants post-LSG and identify the most pertinent histological findings. Materials and methods A literature search was performed to identify relevant case series. Data gathered from relevant case series then underwent statistical analysis. Results The most common histological findings in an LSG specimen were clinically indolent findings such as no pathological abnormalities identified followed by non-specific gastritis. A minority of cases demonstrated clinically actionable findings for which Helicobacter pylori represented the majority of these findings. Conclusion There is a broad spectrum of pathological findings in LSG specimens, ranging from clinically indolent to clinically actionable. The most common histological findings are clinically indolent and only a small portion are of clinical significance and, hence, actionable.

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)0.007
metaresearch head score (Gemma)0.020
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Meta-epidemiology (narrow), Research integrity
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.870
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0070.020
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0110.003
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0010.002
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 head0.195
GPT teacher head0.483
Teacher spread0.288 · 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 designNot applicable
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

Citations6
Published2020
Admission routes1
Has abstractyes

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