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Record W2766291656 · doi:10.14740/wjnu316w

A Case Report of Progressive Multifocal Leukoencephalopathy in Peritoneal Dialysis

2017· article· en· W2766291656 on OpenAlexvenueno aff
Lilia Ben Lasfar, Y. Guedri, Sinda Mrabet, D. Zellama, Anis Hassine, W. Sahtout, A. Azzebi, S. Toumi, Asma Fradi, S. Ben Ammou, Abdellatif Achour

Bibliographic record

VenueWorld Journal of Nephrology and Urology · 2017
Typearticle
Languageen
FieldMedicine
TopicPolyomavirus and related diseases
Canadian institutionsnot available
Fundersnot available
KeywordsMedicinePeritoneal dialysisCorpus callosumLeukoencephalopathyHemiparesisHemodialysisProgressive multifocal leukoencephalopathySurgeryMultiple sclerosisMagnetic resonance imagingPathologyRadiology

Abstract

fetched live from OpenAlex

We experienced a case manifesting progressive multifocal leucoencephalopathy (PML) in peritoneal dialysis (PD). A 27-year-old male patient had received a chronic PD therapy for 4 years. He had a past medical history of hypertension and myelodysplastic syndrome. He complained of hemiparesis with sudden onset and behavioral disorders. The patient seemed indifferent, incoherent with deficit walking. The cranial nerve examination showed a left central facial paralysis. Brain CT scan showed a paramedian right low density area not systemized, located in the corpus callosum and the centrum semiovale. Brain MRI confirmed the presence of PML by the detection of signal abnormalities in bilateral and asymmetrical white matter. The culture of cerebral spinal fluid was negative. The patient underwent six sessions of plasma exchange with favorable evolution. Few cases of PML have been reported in hemodialysis patients and no case has been previously described in PD. In our case, under immunocompromised conditions, precipitating factors appear multifactorial. Depressed immune system induced by chronic dialysis as well as liver disease and myelodysplasia might contribute to the development of florid clinical manifestation of PML. World J Nephrol Urol. 2017;6(3-4):35-39 doi: https://doi.org/10.14740/wjnu316w

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: Case report · Consensus signal: Case report
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.106
Threshold uncertainty score0.336

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
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 head0.017
GPT teacher head0.305
Teacher spread0.288 · 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 designCase report
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
Published2017
Admission routes1
Has abstractyes

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