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Record W4308338527 · doi:10.3390/curroncol29110665

Laparoscopic versus Open Total Gastrectomy for Locally Advanced Gastric Cancer: Short and Long-Term Results

2022· article· en· W4308338527 on OpenAlexvenueno aff
Sara Di Carlo, Leandro Siragusa, Alessia Fassari, Enrico Fiori, Francesca La Rovere, Paolo Izzo, Valeria Usai, Giuseppe Cavallaro, Marzia Franceschilli, Sirvjo Dhimolea, Simone Sibio

Bibliographic record

VenueCurrent Oncology · 2022
Typearticle
Languageen
FieldMedicine
TopicGastric Cancer Management and Outcomes
Canadian institutionsnot available
Fundersnot available
KeywordsMedicineGastrectomyCancerTerm (time)General surgeryLaparoscopySurgeryInternal medicine

Abstract

fetched live from OpenAlex

BACKGROUND: Laparoscopic gastrectomy for early gastric cancer is widely accepted and routinely performed. However, it is still debated whether the laparoscopic approach is a valid alternative to open gastrectomy in advanced gastric cancer (AGC). The aim of this study is to compare short-and long-term outcomes of laparoscopic (LG) and open (OG) total gastrectomy with D2 lymphadenectomy in patients with AGC. METHODS: A retrospective comparative study was conducted on patients who underwent LG and OG for ACG between January 2015 and December 2021. Primary endpoints were the following: recurrence rate, 3-year disease-free survival, 3-year and 5-year overall survival. Univariate and multivariate analysis was conducted to compare variables influencing outcomes and survival. RESULTS: = 0.048). No differences in 3-year and 5-year overall survival; 3-year disease-free survival was improved in the LG group on the univariate analysis but not after the multivariate one. LG was associated with longer operative time, lower blood loss and shorter hospital stay. Lymph node yield was higher in LG. CONCLUSION: LG for AGC seems to provide satisfactory clinical and oncological outcomes in medium volume centers, improved postoperative results and possibly lower recurrence rates.

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: Other design · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.781
Threshold uncertainty score0.730

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 science0.0000.001
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 head0.109
GPT teacher head0.433
Teacher spread0.324 · 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

Citations13
Published2022
Admission routes1
Has abstractyes

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