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Record W4390034366 · doi:10.14740/jocmr5051

Cardiac Amyloidosis Patient With Cardiac Conduction Disturbances

2023· article· en· W4390034366 on OpenAlexvenueno aff
Keisuke Hosono, Shunsuke Kiuchi, Takanori Ikeda

Bibliographic record

VenueJournal of Clinical Medicine Research · 2023
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicAmyloidosis: Diagnosis, Treatment, Outcomes
Canadian institutionsnot available
FundersMinistry of Education, Culture, Sports, Science and Technology
KeywordsMedicineCardiac amyloidosisBiopsyAtrial fibrillationAmyloidosisHeart failureScintigraphyCardiologyRadiologyInternal medicineSurgery

Abstract

fetched live from OpenAlex

Transthyretin cardiac amyloidosis (ATTR-CA) has recently been diagnosed more because of advances in diagnostic techniques, such as 99m Tc-labeled pyrophosphate ( 99m Tc-PYP) scintigraphy. ATTR-CA remains poorly diagnosed by many physicians, except for cardiologists and neurologists, and by patients. In this manuscript, we present a patient who was recommended to undergo a close examination but developed cardiac conduction disturbances and defects due to delays in the examination and treatment initiation. The patient was a 72-year-old Japanese man treated for hypertension at our hospital for approximately 30 years. The patient was diagnosed with left ventricular hypertrophy at 62 years old and hospitalized for heart failure at 68 years old. ATTR-CA was suspected by 99m Tc-PYP scintigraphy performed at 70 years old, and a skin biopsy was performed. However, the skin biopsy did not confirm the diagnosis, and myocardial biopsy was recommended, which was declined by the patient. He finally consented to myocardial biopsy 2 years later, leading to the diagnosis of ATTR-CA at 72 years old. However, the patient had atrial fibrillation and a complete atrioventricular block. If ATTR-CA were widely recognized and understood, it might have been diagnosed and treated before the cardiac conduction disturbances appeared. It is essential to have an understanding and appropriate examinations for ATTR-CA based on sufficient explanation and consent. J Clin Med Res. 2023;15(10-11):456-460 doi: https://doi.org/10.14740/jocmr5051

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)0.002
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 score0.011

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.002
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0020.001
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0030.001

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 head0.136
GPT teacher head0.483
Teacher spread0.347 · 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

Citations2
Published2023
Admission routes1
Has abstractyes

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