Application of Oxygen Therapy and Deep Dhiaphragmatic Breathing to Overcome Shortness of Breath with Acute Decompensated Heart Failure
Bibliographic record
Abstract
Acute heart failure is a term used to indicate a change or event that is very fast where this is characterized by signs or symptoms of heart failure itself so that it can threaten life and life and this needs to be treated immediately The prevalence of heart failure continues to increase every year , The American Heart Association Statistics Update 2021 estimates the prevalence of heart failure to be 6 million of which 1.8 % of the total population of the United States and Canada is 1.5 % to 1.9 % of the population and in Europe 1 % to 2 % (Roger, 2021 ). On the Asian continent, it occupies the highest place due to heart disease deaths with a total of 2,769,000 people. Indonesia ranks second in Southeast Asia with a total of 371 thousand people (WHO, 2017). The purpose of this study was to find out the description of the application of oxygen therapy and Deep Dhiafragmatic Breathing On Mrs. "S" With Problems Acute Decompensated Heart Failure in room Cardiovaskular Care Unit (CVCU) of the Laburan Baji General Hospital, Makassar. The results of the study showed that there was a decrease in shortness of breath after being given oxygen therapy intervention and deep dhiafragma breathing to the client for 3 days of treatment. Where on the first day the respiration rate was 28x/minute and SpO2 was 95%, on the second day the respiration rate was 26x/minute and 97% SpO2, the third day the respiration rate was 24x/minute and SpO2 was 98%. Which indicates amchange in respiratory frequency before and after being given nursing interventions.
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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 | 0.000 | 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 | 0.001 |
| 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".