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Record W2328871448 · doi:10.3748/wjg.v22.i11.3069

Metastatic gastric cancer treatment: Second line and beyond

2016· review· en· W2328871448 on OpenAlexaff
Marwan Ghosn, Samer Tabchi, Hampig Raphaël Kourié, Mustapha Tehfé

Bibliographic record

VenueWorld Journal of Gastroenterology · 2016
Typereview
Languageen
FieldMedicine
TopicGastric Cancer Management and Outcomes
Canadian institutionsCentre Hospitalier de l’Université de Montréal
Fundersnot available
KeywordsIrinotecanMedicineContext (archaeology)CancerIntensive care medicineQuality of life (healthcare)Targeted therapyChemotherapyOncologyInternal medicineColorectal cancer

Abstract

fetched live from OpenAlex

Advanced gastric cancer (aGC), not amenable to curative surgery, is still a burdensome illness tormenting afflicted patients and their healthcare providers. Whereas combination chemotherapy has been shown to improve survival and tumor related symptoms in the frontline setting, second-line therapy (SLT) is subject to much debate in the scientific community, mainly because of the debilitating effects of GC, which would impede the administration of cytotoxic therapy. Recent data has provided sufficient evidence for the safe use of SLT in patients with an adequate performance status. Taxanes, Irinotecan and even some Fluoropyrimidine analogs were found to provide a survival advantage in this subset of patients. Most importantly, quality of life measures were also improved through the use of adequate therapy. Even more pertinent were the findings involving antiangiogenic agents, which would add measurable improvements without significantly jeopardizing the patients' well-being. Further lines of therapy are cause for much more debate nowadays, but specific targeted agents have shown considerable promise in this context. We herein review noteworthy published data involving the use of additional lines of the therapy after failure of standard frontline therapies in patients with aGC.

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 categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.984
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0030.001
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.039
GPT teacher head0.337
Teacher spread0.298 · 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.

Study designOther design
Domainnot available
GenreReview

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

Citations63
Published2016
Admission routes1
Has abstractyes

Explore more

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