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Record W1964003588 · doi:10.1136/ebn.7.3.81

Coaching by non-drug prescribing health professionals reduced total cholesterol concentrations in coronary heart disease

2004· letter· en· W1964003588 on OpenAlexaff
Joan Tranmer

Bibliographic record

VenueEvidence-Based Nursing · 2004
Typeletter
Languageen
FieldMedicine
TopicCardiac Health and Mental Health
Canadian institutionsQueen's UniversityKingston General Hospital
Fundersnot available
KeywordsMedicineCoronary artery diseaseMyocardial infarctionInternal medicine

Abstract

fetched live from OpenAlex

Vale MJ, Jelinek MV, Best JD, et al . Coaching patients On Achieving Cardiovascular Health (COACH): a multicenter randomized trial in patients with coronary heart disease. Arch Intern Med 2003;163:2775–83.[OpenUrl][1][CrossRef][2][PubMed][3][Web of Science][4] Q In patients with coronary heart disease (CHD), does a 6 month programme of coaching by non-drug prescribing nurses and dietitians reduce total cholesterol (TC) concentrations? ### ![Graphic][5]</img>Design: randomised controlled trial (Coaching patients On Achieving Cardiovascular Health [COACH]). ### ![Graphic][6]</img>Allocation: concealed. ### ![Graphic][7]</img>Blinding: blinded (outcome assessors). ### ![Graphic][8]</img>Follow up period: 6 months. ### ![Graphic][9]</img>Setting: cardiology departments of 6 university teaching hospitals in Melbourne, Australia. ### ![Graphic][10]</img>Patients: 792 patients (mean age 59 y, 77% men) who were admitted to hospital for coronary artery bypass graft surgery; percutaneous coronary intervention; acute myocardial infarction or unstable angina and discharged on medical therapy; or coronary angiography with planned … [1]: {openurl}?query=rft.jtitle%253DArchives%2Bof%2BInternal%2BMedicine%26rft.stitle%253DArch%2BIntern%2BMed%26rft.aulast%253DVale%26rft.auinit1%253DM.%2BJ.%26rft.volume%253D163%26rft.issue%253D22%26rft.spage%253D2775%26rft.epage%253D2783%26rft.atitle%253DCoaching%2Bpatients%2BOn%2BAchieving%2BCardiovascular%2BHealth%2B%2528COACH%2529%253A%2BA%2BMulticenter%2BRandomized%2BTrial%2Bin%2BPatients%2BWith%2BCoronary%2BHeart%2BDisease%26rft_id%253Dinfo%253Adoi%252F10.1001%252Farchinte.163.22.2775%26rft_id%253Dinfo%253Apmid%252F14662633%26rft.genre%253Darticle%26rft_val_fmt%253Dinfo%253Aofi%252Ffmt%253Akev%253Amtx%253Ajournal%26ctx_ver%253DZ39.88-2004%26url_ver%253DZ39.88-2004%26url_ctx_fmt%253Dinfo%253Aofi%252Ffmt%253Akev%253Amtx%253Actx [2]: /lookup/external-ref?access_num=10.1001/archinte.163.22.2775&link_type=DOI [3]: /lookup/external-ref?access_num=14662633&link_type=MED&atom=%2Febnurs%2F7%2F3%2F81.atom [4]: /lookup/external-ref?access_num=000187007700015&link_type=ISI [5]: /embed/inline-graphic-1.gif [6]: /embed/inline-graphic-2.gif [7]: /embed/inline-graphic-3.gif [8]: /embed/inline-graphic-4.gif [9]: /embed/inline-graphic-5.gif [10]: /embed/inline-graphic-6.gif

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 categoriesMeta-epidemiology (narrow), Research integrity
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Commentary · Consensus signal: Commentary
Teacher disagreement score0.930
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.004
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 head0.042
GPT teacher head0.374
Teacher spread0.332 · 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

Citations2
Published2004
Admission routes1
Has abstractyes

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