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Record W2921547537 · doi:10.14740/wjnu385

Cancer Antigen 125 and Nephrotic Syndrome

2019· article· en· W2921547537 on OpenAlexvenueno aff

Bibliographic record

VenueWorld Journal of Nephrology and Urology · 2019
Typearticle
Languageen
FieldMedicine
TopicRenal Diseases and Glomerulopathies
Canadian institutionsnot available
Fundersnot available
KeywordsNephrotic syndromeMedicineAnasarcaInternal medicineGastroenterologyGlomerulopathyProteinuriaKidney

Abstract

fetched live from OpenAlex

Background: Cancer antigen 125 (CA-125) serves as a nonspecific test for the diagnosis of several pathologic conditions involving a compromised mesothelium resulting from inflammatory processes. This investigation reports the association of CA-125 with severe nephrotic syndrome caused by primary glomerulopathy, which often coexists with inflammation. Methods: CA-125 levels, clinical data and isolated ultrafiltration therapy were assessed with severe nephrotic syndrome. Nineteen patients were recruited with nephrotic syndrome and primary glomerulopathy. Of these, seven patients had criteria for severe nephrotic syndrome which was defined as including all three of the following criteria: 1) proteinuria is greater than or equal to 8 g/24 h; 2) the presence of anasarca; and 3) creatinine is greater than or equal to 1.2 mg/dL. The nonparametric Spearman Rho and Kruskal-Wallis tests were used to assess these correlations. Results: There was an increase in CA-125 levels in 85% of the patients with nephrotic syndrome. The patients defined as having severe nephrotic syndrome showed a median CA-125 value of 1,501 U/mL (P < 0.001), with levels up to 90-fold greater than normal during the initial assessment. CA-125 ? 700 U/mL was associated with 100% sensitivity and 91% specificity for severe nephrotic syndrome (P < 0.001). Conclusions: CA-125 test is associated quantitatively with the severity of nephrotic syndrome. World J Nephrol Urol. 2019;8(1):14-16 doi: https://doi.org/10.14740/wjnu385

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 score0.076
Threshold uncertainty score0.591

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)0.0010.000

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 head0.008
GPT teacher head0.255
Teacher spread0.248 · 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

Citations3
Published2019
Admission routes1
Has abstractyes

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