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Record W2601589348 · doi:10.4103/1450-1147.203076

The role of bone scintigraphy with single-photon emission computed tomography-computed tomography in the diagnosis and evaluation of calciphylaxis

2017· article· en· W2601589348 on OpenAlexaff
Patrick Martineau, Matthieu Pelletier‐Galarneau, Sadri Bazarjani

Bibliographic record

VenueWorld Journal of Nuclear Medicine · 2017
Typearticle
Languageen
FieldMedicine
TopicParathyroid Disorders and Treatments
Canadian institutionsUniversity of Ottawa
Fundersnot available
KeywordsCalciphylaxisMedicineRadiologySingle-photon emission computed tomographyCalcificationBone scintigraphySoft tissueEmission computed tomographyNuclear medicinePositron emission tomography

Abstract

fetched live from OpenAlex

Calciphylaxis, also known as calcific uremic arteriolopathy, is an uncommon disease, typically found in patients with the end-stage renal disease. Pathophysiological features include small vessel vasculopathy with mural calcification, fibrosis, and thrombosis. The clinical presentation varies but often consists of the necrosis of skin and subcutaneous tissues, with or without visceral organ involvement due to small vessel vasculopathy. This condition has a significant morbidity and mortality, making accurate diagnosis imperative. We present a case of calciphylaxis investigated using planar bone scintigraphy and single-photon emission computed tomography-computed tomography (SPECT-CT). This case stresses the value of SPECT-CT to accurately localize the abnormal uptake in subcutaneous soft tissue microcalcifications and confirms the exact location and extent of pathology.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.003
metaresearch head score (Gemma)0.008
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.004
Threshold uncertainty score0.014

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.008
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0040.001
Science and technology studies0.0000.002
Scholarly communication0.0010.003
Open science0.0010.001
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0010.001

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 head0.027
GPT teacher head0.300
Teacher spread0.272 · 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 source (direct Gemma or distilled Codex), 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

Citations8
Published2017
Admission routes1
Has abstractyes

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