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Record W4242786645 · doi:10.14740/jmc3050e

A Broken Heart: A Rare Complication of Hyponatremia

2018· article· en· W4242786645 on OpenAlexvenueno aff
Adam Purdy, Bibai Ren

Bibliographic record

VenueJournal of Medical Cases · 2018
Typearticle
Languageen
FieldMedicine
TopicTakotsubo Cardiomyopathy and Associated Phenomena
Canadian institutionsnot available
Fundersnot available
KeywordsMedicineEjection fractionHyponatremiaInternal medicineCardiologyTransthoracic echocardiogramCardiomyopathyAnesthesiaHeart failure

Abstract

fetched live from OpenAlex

We present a case of a 47-year-old female with a history of anxiety, depression, and alcohol dependence with history of alcohol withdrawal seizures, who presented after a witnessed seizure episode. She admitted to drinking only copious amounts of black tea 1 week prior to presentation in an attempt to quit alcohol. She was subsequently found to be severely hyponatremic with a serum sodium level of 112 mEq/L and was admitted to the intensive care unit. Approximately 48 h after admission, she experienced sudden-onset of dizziness, diaphoresis, and nausea, with a heart rate of 42 beats/min and blood pressure of 70/40 mm Hg. STAT EKG revealed new T-wave inversions in the lateral and precordial leads and troponin was elevated to 5.99 ng/mL. Treatment was initiated for NSTEMI with continuous heparin infusion, aspirin, clopidogrel, carvedilol, and lisinopril. Transthoracic echocardiogram revealed apical and mid ventricular dyskinesia with hyperkinesis of the basal walls, with an estimated left ventricular ejection fraction of 35%. Cardiac catheterization revealed no significant coronary disease with ejection fraction 30-35% and similar areas of hypo and hyperkinesis, classic for apical ballooning syndrome. This case not only illustrates the rare phenomenon of hyponatremia-induced Takotsubo cardiomyopathy, but is one of the only incidents caused by primary polydipsia. J Med Cases. 2018;9(5):147-150 doi: https://doi.org/10.14740/jmc3050e

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)0.001
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.337
Threshold uncertainty score0.604

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.002
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 head0.030
GPT teacher head0.314
Teacher spread0.285 · 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 designNot applicable
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

Citations4
Published2018
Admission routes1
Has abstractyes

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