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Record W2968255904 · doi:10.1097/jp9.0000000000000026

Management of pancreatic fluid collections in patients with acute pancreatitis

2019· article· en· W2968255904 on OpenAlexaff
Soumya Jagannath Mahapatra, Pramod Kumar Garg

Bibliographic record

VenueJournal of Pancreatology · 2019
Typearticle
Languageen
FieldMedicine
TopicPancreatitis Pathology and Treatment
Canadian institutionsPancreas Centre (Canada)
Fundersnot available
KeywordsAcute pancreatitisMedicinePercutaneousPancreatic pseudocystSurgeryDebridement (dental)PancreatitisLumen (anatomy)Pancreatic abscess

Abstract

fetched live from OpenAlex

Abstract Acute pancreatitis is associated with development of pancreatic fluid collections (PFCs). Acute PFCs that develop in interstitial edematous pancreatitis mostly resolve but some may persist and evolve into pseudocysts. Acute necrotic collections occurring in acute necrotizing pancreatitis generally persist and evolve into walled-off necrosis (WON) after 3 to 4 weeks. Most acute fluid collections do not require drainage unless they are large and cause compression of adjacent organs, contribute to increase in intraabdominal pressure or become infected. Acute infected collections can be managed with antibiotics and percutaneous drainage but may require necrosectomy either by minimally invasive surgical or endoscopic methods such as video-assisted retroperitoneal debridement and percutaneous endoscopic necrosectomy. Mature sterile collections, that is, pseudocyst and WON with a defined wall are best treated by internal transmural drainage which can be achieved either by per-oral endoscopic or surgical, preferably laparoscopic, method. Of late, infected PFCs are increasingly being treated with an endoscopic step-up approach that has been shown to be better than minimally invasive surgical step-up approach in terms of lesser complications. Use of lumen apposing metal stents during endoscopic drainage has emerged as an attractive option that facilitates necrosectomy in infected WON.

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 score0.015
Threshold uncertainty score0.627

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 head0.005
GPT teacher head0.236
Teacher spread0.231 · 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

Citations10
Published2019
Admission routes1
Has abstractyes

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