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The Historical Aspects of Urolithiasis

2023· article· en· W4381153689 on OpenAlexaboutno aff
Oleh Nikitin, Павло Самчук, Oleksii Krasiuk, Andrii Korytskyi, Ihor Komisarenko

Bibliographic record

VenueHealth of Man · 2023
Typearticle
Languageen
FieldMedicine
TopicKidney Stones and Urolithiasis Treatments
Canadian institutionsnot available
Fundersnot available
KeywordsMedicineDiseaseHeredityEthnic groupDemographyUrinary systemInternal medicine

Abstract

fetched live from OpenAlex

The prevalence of urinary stone disease (USD) depends on geographic, climatic, ethnic, dietary, and genetic factors. USD of the upper urinary tract occurs 2–3 times more often in men than in women. The risk of stone formation during the human life is from 5% to 10 %, and the prevalence of USD in different regions varies from 1% to 20%. In the countries with a high life standard (for example, Sweden, Canada, the USA), the prevalence of this disease is very high and is more than 10 %. Unfortunately, in some regions there is the increased indices more than 37 % over the past 20 years. In Ukraine, USD ranks the second place among all urological diseases, more than 52000 patients are registered annually for the first time, and the frequency of occurrence varies from 30% to 45% of all urological pathologies. Mostly young people are affected, the disease progresses with symptoms of acute and chronic pyelonephritis and frequent recurrence of USD (30–80%). This course of USD leads to the development of kidney failure, disability and mortality of patients. The formation of calculi in the kidneys is a complex and polyetiological process that includes endogenous (age, sex and heredity) and exogenous factors (geographical conditions, climate, nutrition). USD has been well known for centuries. It has been proven that humanity has been suffering from this disease for over 7000 years. This is clearly confirmed by various archaeological finds, as well as writings about painful stones and therapeutic procedures that were carried out to remove them. Taking into account the importance and spread of USD, the article presents an overview of the historical development of diagnosis and treatment of this pathology in different regions of the world, an analysis of diagnostic and treatment methods, starting from early antiquity and up to the most modern approaches.

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 score0.367
Threshold uncertainty score0.171

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 head0.038
GPT teacher head0.334
Teacher spread0.296 · 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
Published2023
Admission routes1
Has abstractyes

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