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Record W2775790492 · doi:10.1002/cam4.1274

Medical management of gastric cancer: a 2017 update

2017· review· en· W2775790492 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueCancer Medicine · 2017
Typereview
Languageen
FieldMedicine
TopicGastric Cancer Management and Outcomes
Canadian institutionsPrincess Margaret Cancer CentreUniversity of Toronto
Fundersnot available
KeywordsMedicineCancerOncologyRadiation therapyRandomized controlled trialImmunotherapyInternal medicineClinical trialTargeted therapyChemotherapyIntensive care medicine

Abstract

fetched live from OpenAlex

Gastric cancer remains a considerable health burden throughout the world. The Cancer Genome Atlas (TCGA) analysis has recently unveiled 4 genotypes of gastric cancer with data not ready to change treatment strategy yet. A multimodality approach to therapy is the cornerstone of screening, diagnosing, staging, treating and supporting patients with gastric cancer. The evidence-based approach to localized gastric cancer (>cT1b) is to use an either preoperative or postoperative strategy to maximize the benefit of surgery. The focus of future research is to optimize chemotherapy regimens, determine the role of radiation therapy and investigate the effect of treatment timing. In metastatic gastric cancer, biologic therapies have been introduced targeting markers shown to be prognostic. The results of ongoing randomized controlled phase 3 trials using targeted and immunotherapy agents, either in combination or alone, have the potential to alter the current treatment landscape of advanced gastric cancer.

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.

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 categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.818
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0050.001
Bibliometrics0.0010.001
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0070.000

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 head0.133
GPT teacher head0.448
Teacher spread0.316 · 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