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Record W1989633074 · doi:10.1159/000346410

Oral Sodium Thiosulfate as Maintenance Therapy for Calcific Uremic Arteriolopathy: A Case Series

2013· article· en· W1989633074 on OpenAlexafffundabout
Meteb M. AlBugami, Jo‐Anne Wilson, James R. Clarke, Steven D. Soroka

Bibliographic record

VenueAmerican Journal of Nephrology · 2013
Typearticle
Languageen
FieldMedicine
TopicParathyroid Disorders and Treatments
Canadian institutionsDalhousie University
FundersDalhousie UniversityChongqing University of Arts and Sciences
KeywordsMedicineSodium thiosulfateCohortSurgeryDialysisCalciphylaxisProspective cohort studyDosingInternal medicine

Abstract

fetched live from OpenAlex

BACKGROUND: Calcific uremic arteriolopathy (CUA) is a rare but serious disorder affecting 4% of dialysis patients. Intravenous sodium thiosulfate (IV STS) has been shown as an effective treatment. In Canada, the average cost of IV STS is about CAD 12,000 per month, while the cost of compounded oral STS is CAD 45 per month. METHODS: Prospective cohort where all patients diagnosed with CUA during the year 2011 were included. They were treated initially with IV STS. Afterwards, each patient had a baseline bone scan and was started on oral STS for a total of 6 months followed by a repeat bone scan. A single radiologist, blinded to the dates of both scans for a given patient, read all scans. RESULTS: Four patients were studied. The intravenous dose used was 25 g three times a week for an average duration of 131 days. After the maintenance therapy, 2 patients developed further regression of the lesions, 1 had stable lesions, and 1 got worse; however, nonadherence to the drug was confirmed. The oral medication was well tolerated with no reported side effects. CONCLUSION: Oral STS, after IV STS, seems to stabilize, or even improve CUA lesions, and therefore could be useful as maintenance therapy, especially since its cost is much more reasonable than IV STS and due to the ongoing shortage of the IV formulation.

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.508
Threshold uncertainty score0.516

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 studies0.0000.000
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 head0.288
Teacher spread0.274 · 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

Citations27
Published2013
Admission routes3
Has abstractyes

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