Pengaruh Effortfull Swallow Manuver terhadap Peningkatan Fungsi Menelan pada Pasien Disfagia Paska Stroke di Rumah Sakit Umum Pusat Nasional Dr Cipto Mangunkusumo Jakarta
Bibliographic record
Abstract
Background: Dysphagia is one of the complications that often occurs in stroke patients, which has long-term effects such as dehydration, malnutrition, and aspiration pneumonia, thereby affecting poor quality of life. Objectives: This study aims to determine the effect of the effortful swallow maneuver on improving swallowing function in post-stroke dysphagia patients at Dr. Cipto Mangunkusumo National General Hospital in Jakarta. Methods: This study was a quantitative study with a “One Group Pre-test and Post-test” research design. Statistical tests used the McNemar Test and Pearson's correlation test. The number of respondents in this study was 15 dysphagia outpatients at the Medical Rehabilitation Unit of Dr. Cipto Mangunkusumo National General Hospital in Jakarta. The research instrument used to measure swallowing function was the Toronto Bedside Swallowing Screening Test (TOR-BSST). Results: Before the intervention, 12 respondents were unable to swallow and 3 respondents were able to swallow. After the intervention, 2 respondents were unable to swallow and 13 respondents were able to swallow. There was a significant difference in the swallowing function of respondents before and after the Effortful Swallow Maneuver intervention based on the results of the McNemar Test analysis with a significance value of 0.002. There was a strong and positive correlation between the Effortful Swallow Maneuver and swallowing function based on the results of the Pearson correlation test with a result of 0.705. Conclusion: The Effortful Swallow Maneuver has an effect on improving swallowing function in post-stroke dysphagia patients at Dr. Cipto Mangunkusumo National General Hospital in Jakarta. Keywords: Dysphagia, Effortfull Swallow, Manuver
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 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) | 0.015 | 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".