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Record W2622072006 · doi:10.12968/bjca.2017.12.6.300

The rapid access chest pain clinic (RACPC) and the role of the cardiac physiologist

2017· article· en· W2622072006 on OpenAlexaff
Susan Mathieson, Kelly Victor, Lucy Jarrett-Smith, Michael Marber

Bibliographic record

VenueBritish Journal of Cardiac Nursing · 2017
Typearticle
Languageen
FieldMedicine
TopicCardiac Imaging and Diagnostics
Canadian institutionsSt. Thomas Hospital
Fundersnot available
KeywordsMedicineChest painWorkforceCoronary heart diseaseService (business)NursingMedical emergencyCardiology

Abstract

fetched live from OpenAlex

Coronary heart disease (CHD) is one of the largest killers in the UK. Rapid access chest pain clinics (RACPCs) provide an effective and efficient method for the early diagnosis of patients with CHD. Currently, specialist nurses lead these services. However, there has been a shift, with the focus moving towards cardiac physiologists (CPs) and the role they have to play in delivering these services. The article reflects on the experiences and challenges of a CP's involvement in RACPC. Through these observations and learning points, we aim to encourage other CPs to implement comparable RACPC service delivery systems for the benefit of patients, the broader integrated workforce and the wider healthcare community.

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)0.005
metaresearch head score (Gemma)0.007
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.934
Threshold uncertainty score0.847

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0050.007
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0000.000
Science and technology studies0.0010.002
Scholarly communication0.0010.000
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 head0.015
GPT teacher head0.311
Teacher spread0.296 · 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 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

Citations2
Published2017
Admission routes1
Has abstractyes

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