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Record W2001550610 · doi:10.1002/jso.21269

The role of surgical intervention in the management of duodenal lymphoma

2009· article· en· W2001550610 on OpenAlexaff
Kourosh Sarkhosh, Oliver F. Bathe, Douglas A. Stewart, Lloyd A. Mack

Bibliographic record

VenueJournal of Surgical Oncology · 2009
Typearticle
Languageen
FieldMedicine
TopicLymphoma Diagnosis and Treatment
Canadian institutionsUniversity of Calgary
Fundersnot available
KeywordsMedicinePerforationChemotherapyLymphomaSurgeryStage (stratigraphy)Retrospective cohort studyDemographicsCancerInternal medicine

Abstract

fetched live from OpenAlex

BACKGROUND AND OBJECTIVES: The role of surgical management in duodenal lymphoma is controversial due to the rarity of this tumor subtype. A retrospective review of a provincial cancer registry was performed to assess the rationale for surgical management in duodenal lymphoma. METHODS: Patient demographics, presentations, pathologies, surgical interventions, treatment, and associated disease-specific survival were assessed and descriptively presented. RESULTS: From 1985 to 2005, 23 patients (mean age 58 years [22-82]) were diagnosed. The most common histology was large B-cell lymphoma (74%). A significant proportion presented in a complicated fashion: obstruction (30%), perforation (17%), and hemorrhage (4%). Eight patients (35%) were treated with surgery alone, eight (35%) with surgery and chemotherapy, five (22%) with chemotherapy alone, and two (9%) with supportive care. Of those treated with surgery, indications were mostly emergent conditions including obstruction (58%), perforation (33%), and hemorrhage (8%). Overall median follow-up was 14 months (1-168 months) and overall median survival was 12 months (1-168 months). There were no significant differences in survival by histology, stage, or treatment type. CONCLUSIONS: Chemotherapy continues to represent the therapeutic mainstay for GI lymphomas. However, in duodenal lymphoma, a high proportion of patients require surgery mainly because of complicated presentations.

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: Other design · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.944
Threshold uncertainty score0.168

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 head0.014
GPT teacher head0.316
Teacher spread0.302 · 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 designOther design
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

Citations11
Published2009
Admission routes1
Has abstractyes

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