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Record W2090064623 · doi:10.1159/000323168

Aspirin Adherence, Depression and One-Year Prognosis after Acute Coronary Syndrome

2011· letter· en· W2090064623 on OpenAlexaff
Nina Rieckmann, Matthew M. Burg, Ian M. Kronish, William F. Chaplin, Joseph E. Schwartz, Karina W. Davidson

Bibliographic record

VenuePsychotherapy and Psychosomatics · 2011
Typeletter
Languageen
FieldMedicine
TopicCardiac Health and Mental Health
Canadian institutionsColumbia College
FundersNational Center for Research ResourcesNational Heart, Lung, and Blood InstituteNational Institutes of Health
KeywordsAspirinDepression (economics)Acute coronary syndromeMedicinePsychiatryInternal medicinePsychologyMyocardial infarction

Abstract

fetched live from OpenAlex

In survivors of acute coronary syndromes (ACS; unstable angina or myocardial infarction), depression is highly prevalent [1] and increases the risk of adverse medical outcomes, independent of other prognostic risk markers [2, 3] . After ACS, adherence to recommended medications (e.g. aspirin, statins and -blockers) is crucial to prevent recurrent events or mortality [4–6], yet rates of adherence to these medications are poor [7–9] . Depressed patients are especially likely to be poorly adherent [8–11] . Thus, poor medication adherence may explain some of the increased risk of depression and adverse clinical outcomes after ACS.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Commentary · Consensus signal: none
Teacher disagreement score0.776
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0010.001
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 head0.324
Teacher spread0.291 · 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.

Study designNot applicable
Domainnot available
GenreCommentary

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

Citations18
Published2011
Admission routes1
Has abstractyes

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