Coronary Artery Disease: Medical Management vs. Percutaneous Coronary Intervention
Bibliographic record
Abstract
Mrs.G, a 55 year old female, presented to her family doctor one year ago with chest pain on exertion. The pain was described as sharp in nature, radiating to both arms and was not accompanied by shortness of breath. Symptoms initially appeared following weight lifting at the gym. Currently, she is able to carry out all activities of daily living (ADLs) without limitation. Chest pain is elicited by brisk walking or carrying groceries up stairs, placing her in Canadian Cardiovascular Society (CCS) class I-II. She has a blood pressure of 130/72, heart rate of 60, and BMI of 27. She is a non-smoker with well controlled dyslipidemia. Upon investigation, MIBI stress testing demonstrated moderate-sized inferior wall ischaemia consistent with right coronary artery (RCA) disease. Her current medications include ECASA, atorvastatin, ezetimibe (antilipemic), bisprolol (beta-blocker), perindopril (ACE inhibitor), and a nitroglycerine spray. Following discussion with the patient, it was decided that coronary angiography and possible percutaneous coronary intervention (PCI) was not indicated at this time as symptoms were mild and a satisfactory quality of life was maintained. Mrs.G was encouraged to use her nitroglycerine spray prophylactically.
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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 0.001 |
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".