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Record W2001918336 · doi:10.5430/jst.v2n5p5

Perioperative intraperitoneal chemotherapy for advanced gastric cancer

2012· article· en· W2001918336 on OpenAlexvenueno aff
Antonios-Apostolos Tentes, Nicolaos Pallas, Dimitrios Kyziridis, Georgios Zorbas, Stephanos Popidis, Odysseas Korakianitis, Christos Mavroudis, A. Spyridonidou

Bibliographic record

VenueJournal of Solid Tumors · 2012
Typearticle
Languageen
FieldMedicine
TopicIntraperitoneal and Appendiceal Malignancies
Canadian institutionsnot available
Fundersnot available
KeywordsMedicinePerioperativeGastrectomyCancerIntraperitoneal chemotherapySurgerySurvival rateEPICChemotherapyStage (stratigraphy)GastroenterologyGeneral surgeryInternal medicine

Abstract

fetched live from OpenAlex

Background-Aims: Perioperative intraperitoneal chemotherapy either under normothermia during the early postoperative period (EPIC) or intraoperatively combined with heat (HIPEC) has been shown to improve survival after radical resection of advanced gastric cancer. The purpose of the study is to compare the effect of EPIC and HIPEC in patients undergoing D 2 gastrectomy for advanced gastric cancer. Patients-Methods: Patients that received EPIC after D 2 gastrectomy were retrospectively compared to those that received HIPEC after D 2 gastrectomy. The end point of the study was the assessment of survival, and recurrences. Results: The groups were comparable for age, gender, performance status, tumor anatomic distribution, stage, degree of differentiation, Lauren classification, hospital mortality, morbidity, and type of surgery. 5-year survival rate for HIPEC group was 68% and for EPIC group was 14% ( p =0.0054).The recurrence rate in EPIC group was 57.9% and in HIPEC group 17.4% ( p =0.001). Conclusions: Patients with advanced gastric cancer undergoing D 2 gastrectomy in combination with HIPEC have improved survival and lower recurrence rate as compared to those undergoing D 2 gastrectomy in combination with EPIC.

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 score0.159
Threshold uncertainty score0.499

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
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 head0.016
GPT teacher head0.319
Teacher spread0.303 · 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

Citations0
Published2012
Admission routes1
Has abstractyes

Explore more

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