Bibliographic record
Abstract
Background: Stress testing is a widely used screening tool for finding coronary artery disease, the leading killer of men and women in the United States. Stress tests are a safe noninvasive way to detect coronary ischemia and sift out which patients may benefit from percutaneous coronary intervention or coronary artery bypass graft. This presentation is an overview of the factors to be considered when Stress Testing. Methods: Many factors must be considered before deciding which type of stress test best suits the patient. Time, cost and patient compliance as well as preexisting factors in their medical history such as their electrocardiogram, pacemaker or internal defibrillator, and medications. Keep in mind the following conditions make the EKG portion uninterpretable: LBBB, LVH with strain, repolarization abnormalities secondary to digitalis, baseline abnormalities, and ventricular pacing. If patients are not fit enough to perform an adequate amount of time on a treadmill due to deconditioning or musculoskeletal problems such as arthritis, a pharmacologic stress test should be performed such as a Persantine or Adenosine stress test. When patients are taking heart lowering medication like beta blockers, non-hydropyridine calcium channel blockers, digitalis, or amiodarone, they may have a blunted heart rate response making the EKG portion of the test non-diagnostic. Such patients can be instructed not to take their medication that morning or may consider having a pharmacalogic test instead. Results: Viability studies are sometimes coupled with pharmacalogic stress tests to see if the cardiac muscle is viable and would benefit from perfusion by percutaneous intervention or bypass. These tests are performed by giving the patient sublingual nitroglycerin prior to performing the resting images in a nuclear stress test. Conclusion: Many factors must be taken into consideration before ordering a stress test to see which one would benefit the patient the most. Keep in mind the patients physical condition, medications, and baseline EKG before choosing which one to order.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.023 | 0.010 |
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".