MétaCan
Menu
Back to cohort
Record W2346307629 · doi:10.24095/hpcdp.30.4.05

An investigation of cancer incidence in a First Nations community in Alberta, Canada, 1995–2006

2010· article· en· W2346307629 on OpenAlexaffvenueabout
Amy Colquhoun, Zhichang Jiang, G. Maiangowi, Fredrick D. Ashbury, Yiqun Chen, W. Drobina, Lorraine McLeod, Linda Panaro, S. Sihota, Jordan Tustin, Wadieh Yacoub

Bibliographic record

VenueChronic diseases and injuries in Canada · 2010
Typearticle
Languageen
FieldMedicine
TopicGlobal Cancer Incidence and Screening
Canadian institutionsPublic Health Agency of CanadaHealth CanadaUniversity of CalgaryAlberta Health Services
Fundersnot available
KeywordsIncidence (geometry)Cancer registryCancerDemographyCancer incidenceColorectal cancerPopulationCervical cancerMedicineEnvironmental healthGeographyInternal medicine

Abstract

fetched live from OpenAlex

OBJECTIVE: To determine colorectal and overall cancer incidence as part of a three-pronged investigation in response to the concerns of a First Nations community in Alberta, Canada, located close to sulfur-rich natural gas installations, and to determine whether the incidence of cancers observed in this reserve was higher than expected. METHODS: A population dataset with information identifying First Nations status and band affiliation was linked to the Alberta Cancer Registry to determine cancer incidence cases between 1995 and 2006 for on- and off-reserve study populations. Using indirect standardized incidence ratios, observed cancer incidence cases for the study populations were compared with cases expected based on three separate reference populations. RESULTS: Observed colorectal and overall cancer incidence cases within the First Nations community were not higher than expected. Cervical cancer incidence cases, however, were higher than expected for on- and off-reserve populations; public health measures designed to address this risk have been implemented and on-going surveillance of cancer incidence in the community will be maintained.

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 score0.015
Threshold uncertainty score0.988

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 head0.016
GPT teacher head0.293
Teacher spread0.278 · 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

Citations20
Published2010
Admission routes3
Has abstractyes

Explore more

Same venueChronic diseases and injuries in CanadaSame topicGlobal Cancer Incidence and ScreeningFrench-language works237,207