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Record W2171733960 · doi:10.1136/heart.89.3.349

DO GUIDELINES INFLUENCE PRACTICE?

2003· review· en· W2171733960 on OpenAlexaff
Paul W. Armstrong

Bibliographic record

VenueHeart · 2003
Typereview
Languageen
FieldMedicine
TopicClinical practice guidelines implementation
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsMedicineMEDLINEIntensive care medicine

Abstract

fetched live from OpenAlex

C linical practice guidelines are consensus statements systematically developed to help physicians, and ultimately patients, make decisions about appropriate health care for specific clinical circumstances.Over 20 years ago the American College of Cardiology (ACC) and the American Heart Association (AHA) established a joint task force to define the role of specific, noninvasive and invasive procedures in the diagnosis and management of cardiovascular disease. 1 More specifically, this was initially aimed at establishing the appropriate utilisation of technology in the diagnosis and treatment of cardiovascular patients and was initially directed towards the development of guidelines for permanent cardiac pacemaker implantation.Subsequently, task forces have played an important role in developing other guidelines for a host of cardiovascular, medical, and surgical conditions as well as diagnostic procedures.Using rules of evidence and clinical recommendations originally developed by Sackett for the use of antithrombotic agents, a relatively systematic approach (see box) towards the generation of guidelines has emerged. 2 Framed by three levels of evidence, recommendations are categorised as: (1) data derived from multiple randomised clinical trials; (2) data derived from a single randomised trial or non-randomised studies; and (3) where data does not exist but a consensus opinion is developed from a variety of experts.

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)0.317
metaresearch head score (Gemma)0.767
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: none
Teacher disagreement score0.317
Threshold uncertainty score0.842

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.3170.767
Meta-epidemiology (narrow)0.0010.003
Meta-epidemiology (broad)0.0040.002
Bibliometrics0.0120.015
Science and technology studies0.0060.022
Scholarly communication0.0330.037
Open science0.0090.016
Research integrity0.0310.032
Insufficient payload (model declined to judge)0.0280.010

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 head0.613
GPT teacher head0.651
Teacher spread0.037 · 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.

Study designObservational
Domainnot available
GenreReview

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

Citations22
Published2003
Admission routes1
Has abstractyes

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