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Record W2216197303 · doi:10.14740/jem.v5i6.320

A Natural Fermented Food as a Possible Cause of Syndrome of Inappropriate Secretion of Antidiuretic Hormone

2015· article· en· W2216197303 on OpenAlexvenueno aff
Hidetaka Hamasaki

Bibliographic record

VenueJournal of Endocrinology and Metabolism · 2015
Typearticle
Languageen
FieldMedicine
TopicElectrolyte and hormonal disorders
Canadian institutionsnot available
Fundersnot available
KeywordsHyponatremiaMedicineAntidiureticSyndrome of inappropriate antidiuretic hormone secretionVomitingHormoneInternal medicineUrinary systemEndocrinologyAdverse effectGastroenterologyPediatricsIntensive care medicinePhysiology

Abstract

fetched live from OpenAlex

Syndrome of inappropriate secretion of antidiuretic hormone (SIADH) is caused by various drugs used in routine medical practice. Manda Koso ® (MK) is a fermented food product that contains many natural materials. Since MK is a natural food, it is considered to have no significant adverse effects. However, here we report a case of SIADH suspected of being induced by MK. A 76-year-old woman presenting with headache and vomiting was admitted. Blood examination revealed severe hyponatremia, with a serum sodium level of 120 mEq/L. Based on the results of careful examination, the diagnosis was SIADH. She had no history of central nervous system disorder or lung disease, and therefore drug-induced SIADH was considered. MK was a suspected product. One month after stopping taking MK, her symptoms disappeared, and serum sodium level, urinary sodium excretion, plasma and urinary osmolality, and plasma ADH level had completely normalized. Clinicians should be noted that a natural fermented food product can induce SIADH. J Endocrinol Metab. 2015;5(6):340-341 doi: http://dx.doi.org/10.14740/jem320w

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 score0.003
Threshold uncertainty score0.009

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.001
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0020.001
Insufficient payload (model declined to judge)0.0030.000

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 head0.019
GPT teacher head0.273
Teacher spread0.255 · 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

Citations0
Published2015
Admission routes1
Has abstractyes

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