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Epidemiological Study of Prostate Cancer in the Province of Salamanca (2006-2015)

2022· article· es· W4304117799 on OpenAlexaboutno aff
Javier San Bartolomé Gutiérrez, Antonio Santamaría Abad, Feliciano Sánchez Domínguez, María Dolores Ludeña de la Cruz, Juan Jesús Cruz

Bibliographic record

VenueArchivos Españoles de Urología · 2022
Typearticle
Languagees
FieldMedicine
TopicProstate Cancer Diagnosis and Treatment
Canadian institutionsnot available
Fundersnot available
KeywordsMedicineProstate cancerDemographyIncidence (geometry)EpidemiologyPopulationCancerMortality rateGynecologySurgeryInternal medicineEnvironmental health

Abstract

fetched live from OpenAlex

OBJECTIVES: Knowing the incidence of prostate cancer in Salamanca and its evolution, as well as the age at diagnosis and its evolution. In addition, analyzing the mortality from prostate cancer in the province of Salamanca. METHODS: Descriptive and analytical, longitudinal and retrospective observational study. From the collection of data from the Pathological Anatomy service and the Clinical Documentation service of the Hospital Complex of Salamanca a database was developed for the calculation of incidence rates. The information collected on mortality was obtained through the National Institute of Statistics. For regression analysis, segmented "jointpoint" models were developed. RESULTS: 2676 males diagnosed with prostate cancer were recorded in the province of Salamanca (period 2006-2015). The risk of prostate cancer up to age 74 in 2006 was 6.23%, almost double in 2010. The evolution of mortality rates adjusted to the European population in the province of Salamanca during the period 2006-2015 showed a slight decrease. CONCLUSIONS: In general, Prostate cancer incidence rates increased progressively over the years studied, similar to Spain's overall rates. These rates increased as age progressed. In general, our incidence rates were lower than those reported by the provinces of northern Spain (except Vizcaya) and higher than those recorded by the provinces of southern Spain. In Europe, our rate was surpassed by countries in northern and western Europe and lower than countries in southern and eastern Europe, and part of central Europe. Countries like U.S.A had rates higher than ours, while Canada accounted for a similar rate. On the other hand, mortality rates remained stable during the middle of the study period, suffering from then on a non-statistically significant anual decrease.

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)0.002
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 score0.017
Threshold uncertainty score0.951

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.001
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 head0.019
GPT teacher head0.309
Teacher spread0.290 · 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
Published2022
Admission routes1
Has abstractyes

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