MétaCan
Menu
Back to cohort
Record W4308016405 · doi:10.1186/s12885-022-10206-1

Prevalence of Her2-neu status and its clinicopathological association in newly diagnosed gastric cancer patients

2022· article· en· W4308016405 on OpenAlexfundno aff
Joseph Kattan, Fady El Karak, Fadi Farhat, Dany Abi Gerges, Walid Mokaddem, Georges Chahine, Saad A. Khairallah, Najla Fakhruddin, Jawad A. Makarem, Fadi Nasr

Bibliographic record

VenueBMC Cancer · 2022
Typearticle
Languageen
FieldMedicine
TopicHER2/EGFR in Cancer Research
Canadian institutionsnot available
FundersRoche Canada
KeywordsSurgical oncologyMedicineInternal medicineCancerOncologyAssociation (psychology)General surgery

Abstract

fetched live from OpenAlex

BACKGROUND: This study aimed to report the prevalence of HER2-neu in newly diagnosed early or metastatic gastric cancer (GC) patients, to determine the percentage of patients achieving various IHC scores correlating with the ISH results and to establish a database for GC patients in Lebanon. METHODS: This was a national, multicenter, descriptive and cross-sectional study in patients with histologically confirmed early or metastatic GC newly diagnosed. All eligible patients underwent the IHC and ISH tests in a central laboratory. Demographics, medical history and histopathology data were collected. RESULTS: One hundred fifty-seven patients were included (mean age at diagnosis: 63 ± 14.1 years) during a 3.5 year period. The prevalence of HER2-neu over expression was 21% (95% CI: 15.3-27.4) using ICH and ISH. Agreement between IHC and ISH results was significantly substantial (kappa = 0.681; p-value < 0.001). Over expressed HER2-neu status was significantly associated with high ECOG performance status only. CONCLUSIONS: The prevalence of HER2-neu over expression in newly diagnosed early or metastatic GC patients seemed to be high in Lebanon. The database generated allows to monitor trends in the epidemiology and management of GC.

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)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.007
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
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 head0.064
GPT teacher head0.396
Teacher spread0.332 · 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 designObservational
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
Published2022
Admission routes1
Has abstractyes

Explore more

Same venueBMC CancerSame topicHER2/EGFR in Cancer ResearchFrench-language works237,207