Bibliographic record
Abstract
oronary artery disease is one of the greatest health care problems facing the western industrialised nations.Each year in the UK coronary artery disease is responsible for approximately 125 000 deaths, 274 000 nonfatal myocardial infarctions, and 330 000 new presentations of angina. 1 There have been significant advances in coronary disease treatment over the last few years, with improvements in pharmacological therapy, percutaneous coronary intervention, and bypass surgery.It is therefore increasingly important to identify patients who will benefit from these approaches from the large number of people presenting with chest pain.The cost of identifying this group is putting health care systems under severe financial strain as much as the expense of the treatment itself.The importance of this issue was recognised by the UK government with the publication of its National Service Framework (NSF) for coronary heart disease document in 2000, linked to the provision of significant additional funding. 2 An extensive body of evidence has accumulated over the last 20 years demonstrating the ability of nuclear cardiology techniques, particularly myocardial perfusion imaging (MPI), to identify and risk stratify patients with coronary artery disease.3 Strategies which involve MPI in the investigation of patients with chest pain have been shown to be cost effective as well as clinically effective.4-6 MPI is therefore a potentially valuable tool in a resource limited system such as the National Health Service (NHS) in the UK.This article will attempt to define a realistic evidence based role for MPI in UK cardiology and will then review current practice in terms of both the number and quality of investigations performed.
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 imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.038 | 0.207 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.002 | 0.009 |
| Scholarly communication | 0.016 | 0.018 |
| Open science | 0.004 | 0.006 |
| Research integrity | 0.013 | 0.015 |
| Insufficient payload (model declined to judge) | 0.010 | 0.004 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".