Predictors of long term outcome in medically treated patients with unstable angina.
Bibliographic record
Abstract
BACKGROUND: The choice of invasive or noninvasive strategy for low risk patients with unstable angina is a challenge. OBJECTIVES: To investigate the impact of clinical factors on adverse outcomes in patients receiving successful medical treatment and referred from the hospital without invasive procedures. METHODS: The study group consisted of 166 patients (54% men, age 63+/-11 years) who were discharged symptom free after pharmacological treatment of unstable angina. The authors analyzed demographic, clinical, electrocardiographic, echocardiographic and laboratory parameters. RESULTS: During two years of follow-up, the mortality rate was 4.2%. A composite end point (coronary disease hospitalization, recurrent unstable angina, necessity for revascularization or death) occurred in 99 patients (60%). In multivariate logistic regression, the Canadian Cardiovascular Society (CCS) class (P=0.015) and the left ventricular ejection fraction (P=0.01) were independently predictive for the adverse events. A scoring system was proposed for simple risk stratification, with one point assigned to the patient for CCS class III or IV and left ventricular ejection fraction below 40%, thus yielding a score in the range of 0 to 2. The adverse event rates for total scores of 0, 1 and 2 were 37%, 64% and 86%, respectively. CONCLUSIONS: Uncomplicated follow-up in medically treated patients with unstable angina is rare. Patients with CCS class III and IV or left ventricular ejection fraction below 40% have particularly high rates of recurrent ischemia.
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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 | 0.000 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
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".