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Record W2799895050 · doi:10.1159/000488145

Premature Mortality Due to Malignancies of the Central Nervous System in Canada, 1980–2010

2018· article· en· W2799895050 on OpenAlexaffabout
Truong‐Minh Pham, Khokan C. Sikdar, Winson Y. Cheung, Wilson Roa, Angela Eckstrand, Bethany Kaposhi, Lorraine Shack

Bibliographic record

VenueNeuroepidemiology · 2018
Typearticle
Languageen
FieldMedicine
TopicBrain Metastases and Treatment
Canadian institutionsUniversity of CalgaryAlberta HealthAlberta Health Services
FundersWorld Health Organization
KeywordsMedicineYears of potential life lostDemographyLife expectancyPopulationEpidemiologyLife spanMortality rateGerontologyRelative survivalPediatricsInternal medicineCancer registryEnvironmental health

Abstract

fetched live from OpenAlex

BACKGROUND: In this study, we investigated whether there has been an improvement in premature mortality due to central nervous system (CNS) cancers among the Canadian population from 1980 through 2010. METHODS: Mortality data for CNS cancers were obtained from World Health Organization mortality database. Years of life lost (YLL) was estimated using Canadian life tables. Average lifespan shortened (ALSS) was calculated and defined as the ratio of YLL relative to the expected lifespan. RESULTS: Over this study period, we observed decreases in age standardized rates to the World Standard Population for mortality due to CNS cancers from 5.3 to 4.1 per 100,000 men, and from 3.6 to 2.9 per 100,000 women. Average YLL decreased from 23.6 to 21.5 years of life among men, and from 27.0 to 23.1 years among women in 1980 and 2010, respectively. The ALSS showed that men with CNS cancers lost 30.1% of their life span and women lost 32.5% in 1980, whereas they lost 25.8 and 26.6% in 2010, respectively. CONCLUSION: Our study shows that -Canadian people with CNS cancers have had their lives prolonged at the end of the study period.

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)0.001
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 score0.143
Threshold uncertainty score0.342

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
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 head0.028
GPT teacher head0.272
Teacher spread0.243 · 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
Published2018
Admission routes2
Has abstractyes

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