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Record W2467503646

Effectiveness of Alcohol Septal Ablation in Obstructive Hypertrophic Cardiomyopathy With Versus Without Extreme Septal Hypertrophy.

2016· article· en· W2467503646 on OpenAlexaboutno aff
Yinjian Yang, Chaomei Fan, Jinqing Yuan, Zhimin Wang, Fujian Duan, Shubin Qiao, Shi-jie You, Jiansong Yuan, Fenghuan Hu, Weixian Yang, Xiying Guo, Yishi Li

Bibliographic record

VenuePubMed · 2016
Typearticle
Languageen
FieldMedicine
TopicCardiomyopathy and Myosin Studies
Canadian institutionsnot available
Fundersnot available
KeywordsMedicineAlcohol septal ablationHypertrophic cardiomyopathyInternal medicineCardiologyMuscle hypertrophyCardiomyopathyAblationObstructive cardiomyopathyHeart failure
DOInot available

Abstract

fetched live from OpenAlex

AIMS: Data on the effectiveness of alcohol septal ablation (ASA) in patients with hypertrophic cardiomyopathy (HCM) and extreme septal hypertrophy (ESH) are lacking. This study aimed to compare the effectiveness of ASA in patients with vs without ESH. METHODS: Clinical profiles of 17 patients with ESH and 256 patients without ESH were compared. RESULTS: Baseline pressure gradient and limiting symptoms were comparable between patients with and without ESH. At median 1.1 years of follow-up after ASA, pressure gradient was 48.5 ± 40.4 mm Hg in the ESH group and 40.9 ± 35.2 mm Hg in the non-ESH (N-ESH) group (P=.33). Patients with New York Heart Association class III/IV represented 5.9% of the ESH group and 16.9% of the N-ESH group (P=.39). Patients with Canadian Cardiovascular Society class III/IV represented 5.9% of the ESH group and 10.2% of the N-ESH group (P=.87). CONCLUSION: The effectiveness of ASA seems comparable between patients with and without ESH.

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)0.001
metaresearch head score (Gemma)0.003
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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.001
Threshold uncertainty score0.007

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
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 head0.033
GPT teacher head0.239
Teacher spread0.206 · 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

Citations5
Published2016
Admission routes1
Has abstractyes

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