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Record W3096315319 · doi:10.1055/s-0040-1718886

A Multi-disciplinary Approach to Gastrointestinal Malignancies

2020· article· en· W3096315319 on OpenAlexaboutno aff
Nicholas Fidelman, Peter R. Lokken

Bibliographic record

VenueDigestive Disease Interventions · 2020
Typearticle
Languageen
FieldMedicine
TopicCancer Diagnosis and Treatment
Canadian institutionsnot available
Fundersnot available
KeywordsMedicineMalignancyPsychological interventionGeneral surgeryLiver transplantationIntensive care medicineInternal medicineTransplantation

Abstract

fetched live from OpenAlex

The third quarterly issue of Digestive Disease Interventions is dedicated to topics related to treatment of gastrointestinal malignancies and their complications. The issue opens with a detailed guidance from the team at McGill University in Canada on nerve blocks performed for treatment of abdominal pain and for improvement of periprocedural analgesia for patients undergoing procedures via the transhepatic route. This practice changing report is followed by a detailed discussion (3 manuscripts) on epidemiology, prevention diagnosis, and treatment of venous thromboembolic disease in the setting of malignancy from the world-renowned authorities at University of Pennsylvania and Washington University in St. Louis. The issue also revisits the role of visceral transplantation for patients with cancer (Fujiki et al.), summarizes current thinking regarding liver-directed therapy for metastatic neuroendocrine tumors (Gonzalez-Aguirre A and Ziv E), and provides new insights into resection and ablation for liver tumors (Eskander et al. and Chen et al.). The remainder of the issue discusses treatment of complications of gastrointestinal cancers including malignant bowel obstruction (Litwin et al.), biliary obstruction (Golowa et al.), and ascites (Elsakka and Yarmohammadi). The manuscripts included in this issue of Digestive Disease Interventions Journal were authored by the global thought leaders in the field of interventional oncology, and provide state-of-the-art literature and practice updates.

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 score0.134
Threshold uncertainty score0.617

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
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 head0.112
GPT teacher head0.358
Teacher spread0.246 · 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
Published2020
Admission routes1
Has abstractyes

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