MétaCan
Menu
Back to cohort
Record W2128045351 · doi:10.1155/2014/136984

Amiodarone Induced Hyponatremia Masquerading as Syndrome of Inappropriate Antidiuretic Hormone Secretion by Anaplastic Carcinoma of Prostate

2014· article· en· W2128045351 on OpenAlexaff
Pinaki Dutta, Girish Parthan, Anuradha Aggarwal, Santosh Kumar, Nandita Kakkar, Anil Bhansali, Fabio Rotondo, Kálmán Kovács

Bibliographic record

VenueCase Reports in Urology · 2014
Typearticle
Languageen
FieldMedicine
TopicElectrolyte and hormonal disorders
Canadian institutionsSt. Michael's Hospital
Fundersnot available
KeywordsMedicineAmiodaroneHyponatremiaSyndrome of inappropriate antidiuretic hormone secretionMalignancyEtiologyInternal medicinePathologyHormoneAntidiureticAtrial fibrillation

Abstract

fetched live from OpenAlex

Syndrome of inappropriate antidiuretic hormone secretion (SIADH) is one of the most common causes of hyponatremia. The usual causes are malignancies, central nervous system, pulmonary disorders, and drugs. Amiodarone is a broad spectrum antiarrhythmic agent widely used in the management of arrhythmias. The different side effects include thyroid dysfunction, visual disturbances, pulmonary infiltrates, ataxia, cardiac conduction abnormalities, drug interactions, corneal microdeposits, skin rashes, and gastrointestinal disturbances. SIADH is a rare but lethal side effect of amiodarone. We describe a 62-year-old male who was suffering from advanced prostatic malignancy, taking amiodarone for underlying heart disease. He developed SIADH which was initially thought to be paraneoplastic in etiology, but later histopathology refuted that. This case emphasizes the importance of detailed drug history and the role of immunohistochemistry in establishing the diagnosis and management of hyponatremia due to SIADH.

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)0.001
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.002
Threshold uncertainty score0.005

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.001
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.007
GPT teacher head0.233
Teacher spread0.226 · 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 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

Citations10
Published2014
Admission routes1
Has abstractyes

Explore more

Same venueCase Reports in UrologySame topicElectrolyte and hormonal disordersFrench-language works237,207