Green Tea Gargling Effect on Cough & Hoarseness After Coronary Artery Bypass Graft
Bibliographic record
Abstract
INTRODUCTION: Endotracheal intubation is a method necessary for controlling and maintaining airway during general anesthesia. Cough and hoarseness are common complications after endotracheal intubation. Inflammation has an important role in postoperative cough and hoarseness outbreak. Also it has been stated that green tea has anti-inflammatory properties. Therefore, the current study has been conducted to investigate green tea gargling solution effect on cough and hoarseness after coronary artery bypass graft (CABG) surgery. METHODS: In this single-blind, randomized, & controlled clinical trial, we enrolled 121 patients undergoing CABG admitted to the ICU. The intervention group participants were asked to gargle 30 cc of green tea solution. The control group patients gargled 30 cc of distilled water. An hour after extubation, the intervention group patients were asked to gargle 30 cc of green tea and the control group patients were required to gargle 30 cc of distilled water every 6 hour up to 24 hour (each patient for 4 times). Moreover, the cough and hoarseness questionnaire was also filled in 6, 12, and 24 hours after endotracheal extubation. RESULTS: The results showed no significant differences among the patients in both groups regarding age, gender, body mass index, smoking history, and anesthesia duration. There was a significant difference between the two groups in terms of cough 12 hours after removing the endotracheal tube. At other times, there was no significant difference between the two groups considering cough and hoarseness. CONCLUSION: The present study results showed that green tea gargling does not decrease hoarseness. Though, cough occurrence was less in the intervention group than the other group.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".