A Negative Finding in an Exercise Test Is Reliable among Elderly People: A Follow-Up Study
Bibliographic record
Abstract
BACKGROUND: Coronary heart disease (CHD) is very common among elderly people. OBJECTIVE: To specify the diagnostic value of bicycle exercise tests conducted for elderly patients by trained general practitioners in primary health care. METHODS: We performed a 2-year follow-up study at the Kangasala Health Centre, Finland. The study population comprised all patients at least 60 years old (n = 311) suspected of having CHD without prior diagnosis who were examined by an exercise test carried out by trained general practitioners during a period of 3 years. Specificity and sensitivity as well as positive and negative predictive values of the tests were calculated based on whether or not the participants had a diagnosis of CHD by the end of the follow-up period. The numbers of patients referred for coronary angiography and bypass operation were registered. For analysis, the patients were divided into two age categories: 60-69 years and >or=70 years. RESULTS: A negative finding in the test proved very reliable: CHD manifested in only 3% of these cases in both age groups. The specificity of the test was 72% in the group 60-69 years and 66% in the group >or=70 years; the sensitivity values were 81 and 67%, respectively. The positive predictive values were 26 and 12%, the negative predictive value was 97% in both age groups. 1 in 4 patients yielding a positive finding eventually underwent coronary angiography and 1 in 6 patients coronary bypass surgery. CONCLUSIONS: Exercise tests may predict the clinical outcome of CHD among elderly patients. A negative finding in an exercise test is very reliable.
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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 | 0.002 | 0.008 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".