Cold temperature impairs maximal exercise performance in patients with heart failure: attenuation by acute ACE inhibitor therapy.
Bibliographic record
Abstract
BACKGROUND: Cold exposure decreases ischemic threshold in patients with coronary artery disease and preserved left ventricle (LV) function. The impact of cold exposure and the effect of acute angiotensin-converting enzyme (ACE) inhibitor therapy on maximal exercise capacity have not been studied in patients with symptomatic congestive heart failure. METHODS AND RESULTS: Eleven patients with New York Heart Association class II and III congestive heart failure, aged 61 6 years (mean SD), with LV ejection fraction 25 6%, completed four symptoms-limited maximal exercise tests at 20 C and -8 C in a cold chamber. The exercise tests were performed while the patients were treated with lisinopril for three days, or a placebo. Cold exposure significantly decreased exercise duration in patients treated with a placebo (506 156 s [20 C] versus 419 182 s [-8 C], P<0.01). Rate-pressure products measured at 4 min during the test and at peak performance were significantly increased at -8 C. Patients treated with lisinopril exhibited a significant attenuation of the decrease in exercise time in the cold (-17% for placebo versus -6.6% for lisinopril, P<0.05). CONCLUSIONS: Cold temperature increases cardiac demand in response to exercise and significantly reduces maximal exercise capacity in patients with symptomatic heart failure. Acute treatment with lisinopril attenuates the impact of cold on exercise capacity.
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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 | 0.001 |
| 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 | 0.000 | 0.000 |
| 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".