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Percutaneous ethanol injection treatment of severe hyperparathyroidism in maintenance dialysis: Risks and benefits

2004· article· en· W2023446456 on OpenAlexvenueno aff
Fitsum Guebre‐Egziabher, Firas Easy, Laurent Juillard, Pierre‐Jean Valette, Denis Fouque

Bibliographic record

VenueHemodialysis International · 2004
Typearticle
Languageen
FieldMedicine
TopicParathyroid Disorders and Treatments
Canadian institutionsnot available
Fundersnot available
KeywordsMedicineHyperparathyroidismDialysisSurgeryParathyroidectomySecondary hyperparathyroidismKidney diseaseHemodialysisPerioperativeInternal medicineParathyroid hormone

Abstract

fetched live from OpenAlex

Renal hyperparathyroidism is one of the main and serious complications that occur in chronic kidney disease and particularly during long-term maintenance dialysis. Several recent trials indicate that a high calcium phosphorus product is correlated with high cardiovascular morbidity and mortality and poor outcome. Thus, it is important to improve the control of hyperparathyroidism in chronic renal failure patients. Several methods have been reported for treating severe hyperparathyroidism resistant to medical therapy. Total parathyroidectomy and transplantation or excision of tumor is considered as the treatment of choice. More recently, interventional methods with percutaneous ethanol or calcitriol injection have been developed. These latter techniques have been reported as an alternative to surgical treatment for patients with high perioperative risk. We report the occurrence of laryngeal recurrent nerve palsy, vocal fold paralysis, and hemiplegia after a successful injection of ethanol in a left parathyroid adenoma in a maintenance hemodialysis patient and discuss the restrictions of the procedure and alternative treatments in view of the available studies.

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)0.000
metaresearch head score (Gemma)0.000
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 score0.001
Threshold uncertainty score0.004

Distilled classifier scores by category (both heads)

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 head0.031
GPT teacher head0.290
Teacher spread0.259 · 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

Citations1
Published2004
Admission routes1
Has abstractyes

Explore more

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