MétaCan
Menu
Back to cohort

Characteristics of the Incidence and Prevalence of Chronic Cystitis Among the Male Population in Ukraine

2021· article· en· W3198164102 on OpenAlexaboutno aff
N.O. Saidakova, V.P. Stus, N.V. Havva

Bibliographic record

VenueHealth of Man · 2021
Typearticle
Languageen
FieldMedicine
TopicUrinary Tract Infections Management
Canadian institutionsnot available
Fundersnot available
KeywordsDemographyIncidence (geometry)Quarter (Canadian coin)PopulationEpidemiologyMedicineGeographyPathology

Abstract

fetched live from OpenAlex

The work is devoted to the dynamics of morbidity and prevalence of chronic cystitis among the male population of Ukraine for 10 years (2008–2017). The primary documents were the reported forms of official statistics. The special feature of the study was a comparative analysis of two periods of five-years. The approach was justified by the possibility to trace the nature and intensity of changes, and was also of interest in terms of known territorial changes in the country. It was found that among the total number of registered as well as first-time patients with chronic cystitis in Ukraine, a quarter of them were men. Over the years there has been a decrease in the number of cases. At the same time its rate among the latter is lower than among those registered, which is more pronounced in the last five years. This finding may suggest that the situation is likely to change in the near future towards an increase in the number of cases among men. The first three places in the number of men with chronic cystitis are occupied by the Southeastern, Western, Southern regions. The incidence and prevalence rates (per 100,000) among men are half as high as the corresponding rates among the adult population as a whole. The values of the latter have been decreasing over the years, while the incidence rate increased between 2013 and 2017. Each region has its own peculiarities, which are manifested both by the levels of width in the regions which make up their structure and by the nature of their dynamics. The first identified men with CC usually accounted for one quarter of the total number of cases. Each region is distinguished by the number of first-time offenders. The situation in Ukraine is defined by the Southeastern, Southern regions and Kiev, where the rates are higher than the Ukrainian average and aer increasing.

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 score0.005
Threshold uncertainty score0.173

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 head0.014
GPT teacher head0.290
Teacher spread0.276 · 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

Citations1
Published2021
Admission routes1
Has abstractyes

Explore more

Same venueHealth of ManSame topicUrinary Tract Infections ManagementFrench-language works237,207