Acute coronary syndromes without ST segment elevation
Bibliographic record
Abstract
In the United Kingdom, about 114 000 patients with acute coronary syndromes are admitted to hospital each year.1 More than 5.5 million patients present to a US emergency department with chest pain and other symptoms related to acute coronary syndrome each year.2 Acute coronary syndrome is seen in people of all ages, races, and socioeconomic backgrounds. #### Summary points The diagnosis and management of acute coronary syndromes have been evolving rapidly in recent years. New antithrombotic agents have improved the results of medical treatment, and new methods of estimating a patient's risk of an adverse outcome help clinicians to decide who may benefit from invasive treatment—that is, coronary angiography and subsequent revascularisation (percutaneous coronary intervention or coronary bypass surgery). As these therapeutic decisions need to be made soon after admission, the classification of acute coronary syndromes is now based on the information that is available on admission. Acute coronary syndromes generally represent acute complications of chronic atherosclerotic disease of the coronary arteries. The progressive accumulation of inflammatory materials and lipids over the years can ultimately lead to erosions of the intima or rupture of lipid rich plaques. …
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 0.008 |
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".