Quality of Life in Patients Undergoing Percutaneous Transluminal Coronary Angioplasty (PTCA)
Bibliographic record
Abstract
BACKGROUND: Coronary artery diseases are the main causes of death in industrial Countries. Transluminal angioplasty is a common technique used to manage the condition of coronary arteries. The purpose of this study was to explore the quality of life in patients sustaining this measure in two stages before the procedure and then three consecutive after that 3, 6 and 12 months respectively. MATERIALS & METHODS: This research was a longitudinal study and data was collected between 2011-2013 years. 115 patients were included. Data were collected through using a questionnaire with 40 questions. The subjects before, 3, 6 and 12 months after the procedure filled out questioner. Data were analyzed by statistical tests including T-test, Fisher exact test, Wilcoxon and Friedman with Software SPSS version 16, P value<0.05. RESULTS: There were significant differences in the quality of life in patients with PTCA before procedure and 3 months after that (P=0.004). Quality of life of patients undergoing PTCA in the four levels, three, six and twelve months after the operation had a significant difference (P<0.001). CONCLUSIONS: Quality of life of people with PTCA operation three months after surgery is reduced. It is required during this period the patient treatment team and supports his family and put under the necessary training in this period to give patients and encourage them to pursue their condition should.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".