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Record W3217313677 · doi:10.1136/gutjnl-2021-325266

International variation in oesophageal and gastric cancer survival 2012–2014: differences by histological subtype and stage at diagnosis (an ICBP SURVMARK-2 population-based study)

2021· article· en· W3217313677 on OpenAlexafffundabout
Melina Arnold, Eileen Morgan, Aude Bardot, Mark J. Rutherford, Jacques Ferlay, Alana Little, Bjørn Møller, Oliver Bucher, Prithwish De, Ryan Woods, Nathalie Saint‐Jacques, Anna Gavin, Gerda Engholm, Michael Patrick Achiam, Geoff Porter, Paul M. Walsh, Sally Vernon, Serena Kozie, Agnihotram V. Ramanakumar, Charlotte Lynch, Samantha Harrison, Neil D. Merrett, Dianne L. O’Connell, Tom Mala, Mark Elwood, John Zalcberg, Dyfed Huws, David Ransom, Freddie Bray, Isabelle Soerjomataram

Bibliographic record

VenueGut · 2021
Typearticle
Languageen
FieldMedicine
TopicEsophageal Cancer Research and Treatment
Canadian institutionsMcGill University Health CentreCancerCare ManitobaSaskatchewan Cancer AgencyCanadian Partnership Against CancerNova Scotia Health AuthorityCancer Care Ontario
FundersCancer Council VictoriaPublic Health AgencyCancer Society of New ZealandCancer Institute NSWCancer Research UKScottish GovernmentWorld Health OrganizationKreftforeningenKræftens BekæmpelsePartenariat Canadien Contre Le CancerNational Cancer Registry Ireland
KeywordsStage (stratigraphy)CancerMedicinePopulationInternal medicineCancer survivalVariation (astronomy)GastroenterologyGeneral surgeryPathologyOncologyBiologyEnvironmental health

Abstract

fetched live from OpenAlex

OBJECTIVE: To provide the first international comparison of oesophageal and gastric cancer survival by stage at diagnosis and histological subtype across high-income countries with similar access to healthcare. METHODS: As part of the ICBP SURVMARK-2 project, data from 28 923 patients with oesophageal cancer and 25 946 patients with gastric cancer diagnosed during 2012-2014 from 14 cancer registries in seven countries (Australia, Canada, Denmark, Ireland, New Zealand, Norway and the UK) were included. 1-year and 3-year age-standardised net survival were estimated by stage at diagnosis, histological subtype (oesophageal adenocarcinoma (OAC) and oesophageal squamous cell carcinoma (OSCC)) and country. RESULTS: Oesophageal cancer survival was highest in Ireland and lowest in Canada at 1 (50.3% vs 41.3%, respectively) and 3 years (27.0% vs 19.2%) postdiagnosis. Survival from gastric cancer was highest in Australia and lowest in the UK, for both 1-year (55.2% vs 44.8%, respectively) and 3-year survival (33.7% vs 22.3%). Most patients with oesophageal and gastric cancer had regional or distant disease, with proportions ranging between 56% and 90% across countries. Stage-specific analyses showed that variation between countries was greatest for localised disease, where survival ranged between 66.6% in Australia and 83.2% in the UK for oesophageal cancer and between 75.5% in Australia and 94.3% in New Zealand for gastric cancer at 1-year postdiagnosis. While survival for OAC was generally higher than that for OSCC, disparities across countries were similar for both histological subtypes. CONCLUSION: Survival from oesophageal and gastric cancer varies across high-income countries including within stage groups, particularly for localised disease. Disparities can partly be explained by earlier diagnosis resulting in more favourable stage distributions, and distributions of histological subtypes of oesophageal cancer across countries. Yet, differences in treatment, and also in cancer registration practice and the use of different staging methods and systems, across countries may have impacted the comparisons. While primary prevention remains key, advancements in early detection research are promising and will likely allow for additional risk stratification and survival improvements in the future.

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 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 score0.005
Threshold uncertainty score1.000

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)0.0010.000

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 head0.045
GPT teacher head0.342
Teacher spread0.297 · 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

Citations56
Published2021
Admission routes3
Has abstractyes

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