[Yield of a coronary arteriography database. A study of 5.536 registrations at the cardiologic laboratory, Rigshospitalet].
Bibliographic record
Abstract
INTRODUCTION: Coronary arteriography (CAG) is an expensive investigation that provides potentially valuable information, but also carries a risk of severe complications. It is therefore natural to examine the usefulness of an existing database on CAG. METHODS: The analysis covers all registrations of CAG entered into the database at the Heart Centre at Rigshospitalet, Copenhagen, from April 1999 to September 2000. RESULTS: Altogether, 5536 CAGs were registered. The indication was stable coronary artery disease in 52.0% and acute ischaemic heart disease in 25.5%. As an example of the medical information available from the data base, it is notable that left main coronary stenosis or three-vessel disease, conditions in which coronary bypass surgery increases long-term survival, was found in 42.4% of patients with angina pectoris in Canadian Cardiovascular Society (CCS) class 4, but also in 24.4% of patients in CCS class 1. DISCUSSION: Clinical databases, such as the one presented, can ensure that all relevant information is stored, and in this case even results in enhanced effectiveness, because data may be directly transformed into other formats, such as charts. The database furthermore provides clinical information, for instance that the severity of angina pectoris cannot identify the most ill patients in whom a CABG is potentially life-prolonging. In addition, the database provides administrative data that is used in the training of doctors, evaluation of referral patterns, surveillance of complications, and in the daily administration and planning.
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 | 0.006 | 0.025 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.009 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 0.006 |
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".