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Record W2075209389 · doi:10.3747/co.v17i3.466

Peritoneal Seeding and Subsequent Progression of Mantle Cell Lymphoma after Splenectomy for Debulking

2010· article· en· W2075209389 on OpenAlexvenueno aff
Gülistan Bahat, Bülent Saka, Mustafa Nuri Yenerel, Erkan Yılmaz, Öner Doğan

Bibliographic record

VenueCurrent Oncology · 2010
Typearticle
Languageen
FieldMedicine
TopicIntraperitoneal and Appendiceal Malignancies
Canadian institutionsnot available
Fundersnot available
KeywordsMedicineMantle cell lymphomaSplenectomyDebulkingSeedingTumor DebulkingLymphomaSurgeryPathologyInternal medicineSpleenCancerChemotherapyBiologyOvarian cancer

Abstract

fetched live from OpenAlex

BACKGROUND: Peritoneal seeding after abdominal surgery is a well known route of metastasis in intra-abdominal solid tumours. Direct mechanical contamination, local peritoneal trauma and subsequent inflammation, postoperative immunosuppression, and laparoscopic surgery are the proposed predisposing factors for this type of metastasis. These factors probably result in enhanced adhesion or growth of tumour cells. However, this route of metastasis has not yet been reported for lymphomas. Here, we report the first case of peritoneal seeding of lymphoma cells after an abdominal surgery. CASE DESCRIPTION: A 47-year-old man with mantle cell lymphoma had ascites because of infiltration of the liver. He underwent debulking splenectomy. The postoperative ascites cytology and control abdominal computed tomography imaging both confirmed peritoneal involvement and lymphoma progression. Demonstration of negative peritoneal involvement before surgery and close timing of peritoneal involvement after splenectomy suggested to us that the debulking surgery was the main cause of peritoneal seeding of lymphoma cells in our case. CONCLUSIONS: Factors similar to those in solid tumour seeding may also be valid for lymphomas. Peritoneal seeding and consequent disease progression may be a potential complication of abdominal surgery in lymphoma with extensive intra-abdominal involvement.

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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.386
Threshold uncertainty score0.414

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
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 head0.040
GPT teacher head0.375
Teacher spread0.335 · 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 designBench or experimental
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

Citations7
Published2010
Admission routes1
Has abstractyes

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