PENGARUH TEKNIK PERNAFASAN BUTEYKO TERHADAP PENURUNAN FREKUENSI KEKAMBUHAN ASMA PADA PASIEN PENDERITA ASMA
Bibliographic record
Abstract
Asthma is a disease that is very close to the community and has an ever-increasing population. The prevalence of asthma in Indonesia is 3.5 % . Prevalence of asthma in Bengkulu, especially Rejang Lebong in 2012 amount 1.606 people. Higher data in Kampung Delima Working Area Health Center are 182 peoples and theres 53 people (29,12%) feels recurrence. This study aimed to determine the effect of buteyko breathing techniques to decrease the frequency of recurrence of asthma in asthmatics in Kampung Delima Working Area Health Center. This study was pre Experimental design using one group pre and post test. Populations of this study are 182 people. The samples were taken with accidental sampling technique which amounts to 30 people. The distribution of the average frequency of recurrence of asthma before being given Buteyko breathing technique is 3.40 and the average frequency after the Buteyko breathing technique given is 2.07 . Based on the analysis of test data is obtained , which means there is the influence of the Buteyko breathing technique to decrease the frequency of recurrence of asthma in the Work Area Kampung Delima health center (p=0,000).
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.016 | 0.003 |
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".