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Record W4318042568 · doi:10.3390/cancers15030714

Effects of Tumor Localization, Age, and Stage on the Outcomes of Gastric and Colorectal Signet Ring Cell Adenocarcinomas

2023· article· en· W4318042568 on OpenAlexaff
Matthew G.K. Benesch, Alexander Mathieson, Shalana O’Brien

Bibliographic record

VenueCancers · 2023
Typearticle
Languageen
FieldMedicine
TopicGastric Cancer Management and Outcomes
Canadian institutionsMemorial University of Newfoundland
FundersNational Cancer Institute
KeywordsMedicineSignet ring cellColorectal cancerAdenocarcinomaEpidemiologyRectumInternal medicineStage (stratigraphy)GastroenterologyOncologyStomachCancerPopulationDiseaseColorectal adenocarcinomaPathologyBiology

Abstract

fetched live from OpenAlex

Signet ring cell adenocarcinomas (SRCCs) are a rare histological adenocarcinoma subtype, classically thought to have a worse prognosis than conventional adenocarcinomas. The majority of these cancers occur in the stomach, colon, and rectum. Their rarity means that most epidemiological studies into their pathology are often underpowered, and interpretations from these reports are mixed. In this study, we use the Surveillance, Epidemiology, and End Results Program (SEER) database to examine the effects of tumor localization, age, and stage on gastric and colorectal cancer outcomes. For early onset localized and regional gastric cancers, SRCCs have the same overall risk of mortality compared to conventional adenocarcinomas. Over the age of 50 years, SRCCs have worse outcomes across all stages. Gastric SRCCs are 2-3-fold more likely in younger patients, and more heavily favor the distal stomach. Like conventional adenocarcinomas, proximal gastric SRCCs have decreased survival. Across all ages, stages, and locations, colorectal SRCCs have worse outcomes. SRCCs favor the right colon, but outcomes are significantly worse for the left colon and rectal cancers. Relative to adenocarcinomas, colorectal SRCCs have the worst outcomes in younger patients. Overall, these results provide insights into SRCC disease patterns that cannot be surmised outside of population-level data.

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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.005
Threshold uncertainty score0.262

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 science0.0000.000
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 head0.013
GPT teacher head0.243
Teacher spread0.230 · 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 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

Citations15
Published2023
Admission routes1
Has abstractyes

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