MétaCan
Menu
Back to cohort
Record W28660552 · doi:10.1007/s11886-017-0876-4

A community reference grammar of Labrador Inuttitut

2009· article· en· W28660552 on OpenAlexaboutno aff
Alana Johns, Christine Nochasak

Bibliographic record

VenueCurrent Cardiology Reports · 2009
Typearticle
Languageen
FieldComputer Science
TopicNatural Language Processing Techniques
Canadian institutionsnot available
Fundersnot available
KeywordsGrammarComputer scienceLinguisticsRule-based machine translationSpeech communityArtificial intelligenceNatural language processing

Abstract

fetched live from OpenAlex

As patients continue to live longer after myocardial infarction (MI), the prevalence of post-MI heart failure continues to rise. Changes in the left ventricle (LV) after MI involve complex interactions between cellular and extracellular components, under neurohormonal regulation. Treatments to prevent adverse LV remodeling and promote reverse remodeling in the post-MI setting include early revascularization, pharmacotherapy aimed at neurohormonal blockade, and device-based therapies that address ventricular dyssynchrony. Despite varying definitions of adverse LV remodeling examined across multiple imaging modalities, the presence of an enlarged LV cavity and/or reduced ejection fraction is consistently associated with poor clinical outcomes. Advances in our knowledge of the neurohormonal regulation of adverse cardiac remodeling have been instrumental in generating therapies aimed at arresting adverse remodeling and promoting reserve remodeling. Further investigation into other specific mechanisms of adverse LV remodeling and pathways to disrupt these mechanisms is ongoing and may provide incremental benefit to current evidence-based therapies.

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)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.934
Threshold uncertainty score0.497

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 science0.0010.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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 head0.035
GPT teacher head0.318
Teacher spread0.282 · 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 designOther design
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

Citations1
Published2009
Admission routes1
Has abstractyes

Explore more

Same venueCurrent Cardiology ReportsSame topicNatural Language Processing TechniquesFrench-language works237,207