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Record W3033369774 · doi:10.1111/jrh.12471

The Impact of Geographic Location on Saskatchewan Prostate Cancer Patient Treatment Choices: A Multilevel and Spatial Analysis

2020· article· en· W3033369774 on OpenAlexaffabout
Mustafa Andkhoie, Michael Szafron

Bibliographic record

VenueThe Journal of Rural Health · 2020
Typearticle
Languageen
FieldMedicine
TopicProstate Cancer Diagnosis and Treatment
Canadian institutionsUniversity of Saskatchewan
Fundersnot available
KeywordsMedicineDemographyOddsCovariateMultilevel modelProstate cancerRural areaLogistic regressionWatchful waitingOdds ratioGerontologyCancerInternal medicineStatistics

Abstract

fetched live from OpenAlex

OBJECTIVE: The purpose of this study was to estimate the relationship between remoteness and the initial chosen treatment (active surveillance/watchful waiting (AS/WW), radiation therapy (RT), surgery, chemotherapy (CT), or hormonal therapy (HT) for prostate cancer (PCa). METHODS: This study built 2 multilevel generalized linear models via a binomial link for each treatment type (one with only covariates and one with 2 additional study variables to the covariate model). The study also used cluster analysis using the Global and local Moran's I spatial statistics to find any complementary results to the above models. RESULTS: This study found that patients living in the rural areas have lower odds (OR = 0.59; 95% CI, 0.45-0.77; P < .001) of having surgery compared to patients living in the greater urban areas. Among patients whose closest PCa assessment center is Regina, patients living in the greater urban areas have higher odds (OR = 1.66; 95% CI, 1.03-2.68; P = .039) of choosing RT compared to patients living in the rural areas. There was no statistically significant effect of remoteness on whether one chose HT or AS/WW. CONCLUSIONS: There are regional disparities to PCa treatment utilization. Living in rural areas affects choosing surgery and, in certain localized geographical regions, affects choosing RT. For non-curative treatments (ie, AS/WW and HT), we did not find any association with geographical remoteness.

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.528
Threshold uncertainty score0.975

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 head0.023
GPT teacher head0.335
Teacher spread0.312 · 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

Citations8
Published2020
Admission routes2
Has abstractyes

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