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Record W2767376996 · doi:10.28984/drhj.v1i0.25

Evaluation of the impact of various definitions of rurality on the prediction of prostate cancer progression

2017· article· en· W2767376996 on OpenAlexaffvenueabout
Nancy Lightfoot, Bruce Oddson, Colin Berriault, Robert M. Lafrenie, Jacques Abourbih, John R. MacDonald, Roger Strasser

Bibliographic record

VenueDiversity of Research in Health Journal · 2017
Typearticle
Languageen
FieldMedicine
TopicGlobal Cancer Incidence and Screening
Canadian institutionsNOSM UniversityCancer Care OntarioLaurentian University
Fundersnot available
KeywordsRuralityProstate cancerMedicineResidenceCohortProportional hazards modelDemographyRural areaGerontologyChartCancerStatisticsInternal medicineMathematicsPathology

Abstract

fetched live from OpenAlex

This study evaluates whether three definitions of rural and urban residence predict prostate cancer progression. People were classified as urban or rural using three definitions: rural and small town (RST), Beale's rural-urban continuum codes, and the Rurality Index of Ontario (RIO) 2008 score. This was a chart-based cohort study of males with prostate cancer who underwent external beam radiation therapy (EBRT) in the Regional Cancer Program at Health Sciences North in Sudbury, Ontario from 1996 to 2003. Data indicative of each of the three definitions were used as predictors in Cox regression analysis for the period of 1,000 to 3,000 days after initial diagnosis and as the basis for dichotomous strata in a log rank test.
 Complete data were acquired from 629 charts. There was no significant association between any of the three definitions of rurality and prostate cancer progression. However, a Beale-based dichotomization led to survival differences using the log rank test. Beale stratification was potentially sensitive to relevant differences in populations that were not represented by the other two definitions. Given the moderate correlations between the different rurality scores, there may be merit to considering multiple rurality scores as they may lead to different cancer progression outcomes in some situations.

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)0.012
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 score0.024
Threshold uncertainty score0.982

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0120.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
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 head0.629
GPT teacher head0.576
Teacher spread0.053 · 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

Citations0
Published2017
Admission routes3
Has abstractyes

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