Diagnostic Value of Electrocardiographic ST-T Wave Changes in Lead aVL in Patients with Chronic Stable Angina
Bibliographic record
Abstract
Background: coronary artery diseases affect millions of patients in the world which cause a lot of complications up to death. by very simple non-invasive method like ECG we can predict and early diagnose and even localize very serious lesion in coronary angiograghy in patient with chronic stable angina and this is the target of our research. Aim of Work: this study aimed to detect the diagnostic value of ST segment and T wave changes in lead AVL in patient with chronic stable angina. Methods: this was a prospective observation study that was done from June 2017 to July 2018 and it included 200 patients presented to Alhussin University Hospital or referred to us from other secondary hospitals with chronic stable angina. All patients were subjected to full history, clinical examination, ECG and coronary angiograms with focusing on ST segment and T wave changes and analyses and localized the lesions in coronary angiograms and their relationship to previous ECG changes. Results: mean age in patients in our study was 55.89 years old with 77% males and 23% females. The most common risk factor was HTN. There was a relationship between ST-T wave changes in lead AVL in patient with chronic stable angina with a moderate agreement to mid LAD lesion 78% and to lesser degree LCX then RCA and LM. Conclusion: according to the previous results of our research, inverted T wave in lead AVL in chronic stable angina corresponds angiographically to tight mid left anterior descending coronary artery lesion.
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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 | 0.001 | 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".