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Record W2333616930 · doi:10.1097/mpa.0000000000000147

The Current State of Pancreatic Cancer in Canada

2014· article· en· W2333616930 on OpenAlexaffabout
Scott Hurton, Frank M. MacDonald, Geoff Porter, Mark Walsh, Michele Molinari

Bibliographic record

VenuePancreas · 2014
Typearticle
Languageen
FieldMedicine
TopicPancreatic and Hepatic Oncology Research
Canadian institutionsDalhousie University
Fundersnot available
KeywordsMedicineIncidence (geometry)Confidence intervalPancreatic cancerCase fatality rateRelative survivalCancer registryCancerMortality rateInternal medicineSurgeryDemographyEpidemiology

Abstract

fetched live from OpenAlex

OBJECTIVE: This study aimed to evaluate the trends in the incidence, survival, and surgical therapy for Canadian patients affected by pancreatic cancer (PC). METHODS: The incidence, mortality, number of resections, and outcomes of patients with PC stratified by year, sex, and province were extracted from Canadian cancer databases. RESULTS: In 2012, PC was diagnosed in 4600 Canadians and it was responsible for 4300 deaths. The age-standardized incidence was 9 to 10 new cases per 100,000 individuals. The mortality rate remained the highest among all the solid tumors with a case-to-fatality ratio of 0.93. The age-standardized 5-year relative survival was 9.1% (95% confidence interval [CI], 8.3-10). There were geographic variations among provinces with the highest survival registered in Ontario (10.9%; 95% CI, 9.9-12) and the lowest survival reported in Nova Scotia (4.7%; 95% CI, 2.8-7.2). The percentage of patients who underwent surgery decreased from 19% (2006-2007) to 17% (2009-2010). Pancreatic resections were performed in high-volume centers in 74% of cases. In-hospital mortality was 5%, 93% of patients were discharged home, and 36% of patients required home support after discharge. CONCLUSIONS: Long-term outcomes of Canadian patients affected by PC remain unsatisfactory, with only 9% of the patients surviving at 5 years. Surgical therapy was performed only in 17% to 19% of patients.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.260
Threshold uncertainty score0.577

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
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 head0.021
GPT teacher head0.329
Teacher spread0.309 · 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

Citations24
Published2014
Admission routes2
Has abstractyes

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