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Record W2588258474 · doi:10.1093/ije/dyw256

Cohort Profile: Alberta’s Tomorrow Project

2016· article· en· W2588258474 on OpenAlexaffabout
Ming Ye, Paula J. Robson, Dean T. Eurich, Jennifer E. Vena, Jianyi Xu, Jeffrey Johnson

Bibliographic record

VenueInternational Journal of Epidemiology · 2016
Typearticle
Languageen
FieldMedicine
TopicGlobal Cancer Incidence and Screening
Canadian institutionsAlberta Health ServicesUniversity of Alberta
Fundersnot available
KeywordsCohortCohort studyMedicineGeographyInternal medicine

Abstract

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Why was the cohort set up?Alberta, a western province in Canada, has a population of approximately 4.2 million.1 Over 30% of Albertans report suffering from at least one chronic condition, and the prevalence increases to 75% for seniors aged 65 and older. 2 Chronic diseases, including cancer, cardiovascular diseases, diabetes and chronic obstructive pulmonary diseases, account for over 60% of deaths in Alberta.3,4 Nearly 50% of all health care expenditure in Alberta comes from chronic diseases, which is placing tremendous burden on Alberta's health care systems.5,6 As public health and health care systems are facing substantial challenges of chronic diseases, 5,7 there is increasing recognition that chronic disease research should focus on prevention.[8][9][10] Specifically, more research is needed to learn: (i) how modifiable risk factors interact with social and environmental factors and genetic predisposition at multidimensional levels; 11-13 and (ii) how health care services, along with modifiable risk factors, impact on incidence and health outcomes of chronic diseases.[14][15][16] The latter will help to develop new strategies to improve the role of health care systems in chronic disease prevention.Previously established large cohort studies, such as the British Doctors Study, 17 Framingham Heart Study 18 and Nurses' Health Study, 19 have demonstrated feasibility and methodological advantages and their potential as research platforms to improve our understanding of chronic diseases.20,21 Recognizing the value of large cohort studies in chronic disease research, Alberta, with the fourth largest population in Canada and a particular publicly funded, provincially administered health care system, has taken a national, and potentially international leadership role in chronic disease research with the establishment of the Alberta's Tomorrow Project (ATP), a large province-wide cohort study of cancer and chronic diseases.22 The goal of ATP is to provide a platform to support the aetiological study of cancer and other chronic diseases.22 Another objective of ATP is to link cohort data with provincially managed health care databases and other health records.Successfully co-analysing cohort data and administrative health care data will allow ATP to study patterns of health care utilization and their associations with risks and outcomes of chronic diseases.23 The expanded dataset will serve as a unique research platform for chronic disease aetiological study, health services research and future population-level intervention studies.23 Findings of ATP cohort study will provide policy makers scientific evidence on which to base holistic strategies to prevent cancer and other chronic diseases.Alberta's Tomorrow Project, affiliated with CancerControl Alberta, Alberta Health Services, is located at Richmond Road Diagnostic and Treatment Centre in Calgary, Canada.ATP is funded by Alberta

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.005
Version: metacan-v3-hybrid-931329e0061cValidation 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: none
Teacher disagreement score0.907
Threshold uncertainty score0.187

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.005
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0040.007
Science and technology studies0.0020.000
Scholarly communication0.0020.000
Open science0.0020.002
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0150.004

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 head0.176
GPT teacher head0.453
Teacher spread0.277 · 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 source (direct Gemma or distilled Codex), 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

Citations73
Published2016
Admission routes2
Has abstractno

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