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Record W2140552840 · doi:10.1080/09603123.2011.634386

A geographical analysis of breast cancer clustering in southern Ontario: generating hypotheses on environmental influences

2011· article· en· W2140552840 on OpenAlexafffundabout
Isaac Luginaah, Kevin M. Gorey, Tor H. Oiamo, Kathy Tang, Eric J. Holowaty, Caroline Hamm, Frances C. Wright

Bibliographic record

VenueInternational Journal of Environmental Health Research · 2011
Typearticle
Languageen
FieldMedicine
TopicData-Driven Disease Surveillance
Canadian institutionsSunnybrook Health Science CentreUniversity of TorontoHealth Sciences CentreWindsor Regional HospitalCancer Care OntarioUniversity of WindsorWestern University
FundersCanadian Institutes of Health Research
KeywordsBreast cancerRelative riskStatisticCancerMedicineGeographyCluster analysisDemographyEnvironmental healthStatisticsInternal medicineMathematics

Abstract

fetched live from OpenAlex

This article presents the results of spatial analysis of breast cancer clustering in southern Ontario. Data from the Cancer Care Ontario were analyzed using the Scan Statistic at the level of county, with further analysis conducted within counties that were identified as primary clusters at the dissemination area level. The results identified five counties as primary clusters of women diagnosed with breast cancer between 1986 and 2002: Essex (relative risk [RR] =1.096-1.061; p<0.001), Lambton (RR=1.05-1.167), Chatham-Kent (RR=1.133-1.191), Niagara (RR=1.228-1.290) and Toronto (RR=1.152-1.146). The within county analysis revealed several DAs with significantly higher (RR>3, p<0.05) rates of breast cancer, and supports our hypothesis that breast cancer risk in southern Ontario may be associated with industrial and environmental (such as pesticides) pollutants. Further research is needed to verify the environmental links within the identified clusters.

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)0.000
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 score0.021
Threshold uncertainty score0.998

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0030.000

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 head0.104
GPT teacher head0.393
Teacher spread0.289 · 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

Citations18
Published2011
Admission routes3
Has abstractyes

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