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Record W4378745883 · doi:10.1097/mcg.0000000000001871

Management of Pyogenic Liver Abscesses

2023· article· en· W4378745883 on OpenAlexaff
John C. Lam, William Stokes

Bibliographic record

VenueJournal of Clinical Gastroenterology · 2023
Typearticle
Languageen
FieldMedicine
TopicAmoebic Infections and Treatments
Canadian institutionsProvincial Laboratory of Public HealthUniversity of Alberta
Fundersnot available
KeywordsMedicinePyogenic liver abscessLiver abscessGeneral surgerySurgeryAbscess

Abstract

fetched live from OpenAlex

Pyogenic liver abscesses (PLAs) are a suppurative infection of the hepatic parenchyma responsible for significant morbidity and mortality. PLAs are categorized into a variety of mechanisms: (1) via the portal vein, (2) through the biliary tract, (3) via the hepatic artery, (4) from trauma, (5) contiguously via direct extension, and (6) cryptogenically. The pathogenesis of PLA, which informs treatment, can often be discerned based on host factors, clinical presentation, and causative microorganisms. The Streptococcus anginosus group, hypervirulent Klebsiella pneumoniae , and multidrug-resistant gram-negative pathogens have emerged as microbiologically challenging organisms to treat. The identification of hypervirulent K. pneumoniae should prompt for assessment for metastatic spread and consideration of prolonged antimicrobial treatment. Abdominal imaging is indispensable in characterizing PLAs and facilitating source control interventions. Source control remains the most critical aspect of PLA management, followed by antimicrobial therapy. Empiric antibiotics for PLAs are informed by the suspected etiology of PLA formation. Duration of antimicrobial therapy is individualized and dependent on multiple components, including the success of achieving source control, host factors, mechanism of PLA development, and the illness course of the individual-factoring in clinical, biochemical, and radiographic parameters.

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)0.001
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 score0.208

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
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 head0.069
GPT teacher head0.415
Teacher spread0.346 · 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

Citations19
Published2023
Admission routes1
Has abstractyes

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