The Presence of Wind Worsens the Effect of Cold Temperature on Time to Ischemia in Patients with Stable Angina
Bibliographic record
Abstract
PURPOSE: This study tested the hypothesis that the combination of cold temperature and wind further reduces time to ischemia during treadmill stress testing compared with cold temperature alone. METHODS: Eighteen participants (56 ± 9 yr) with stable angina performed four treadmill stress tests in a randomized crossover design at +20°C and -8°C, with and without a 24-km·h -1 headwind. Time to ischemia (≥1-mm ST-segment depression) and angina, rate pressure product, and total exercise duration were determined. RESULTS: At -8°C, time to ischemia was reduced by 22% (-58 s (-85 to -31 s), P < 0.01) compared with +20°C. The addition of wind at -8°C reduced time to ischemia by a further 15% (-31 s (-58 to -4 s) vs -8°C without wind, P = 0.02). The addition of wind did not affect time to ischemia at +20°C ( P = 0.38). Cold temperature and wind did not affect time to angina ( P = 0.46 and P = 0.61) or rate pressure product ( P = 0.46 and P = 0.09). Total exercise time was reduced in the presence of wind at -8°C (-29 s (-51 to -7 s), P = 0.01), but not at +20°C ( P = 0.09). CONCLUSIONS: The presence of wind reduces time to ischemia when exercise stress testing is performed in a cold environment. These results suggest that wind should be considered when evaluating the risks posed by cold weather in patients with coronary artery disease and exercise-induced 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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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) | 0.002 | 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".