GAMBARAN PENGELOLAAN KLIEN DENGAN BERSIHAN JALAN NAPAS TIDAK EFEKTIF PADA KASUS TUBERKULOSIS PARU DI RUANG YUDISTIRA RSUD SANJIWANI GIANYAR TAHUN 2021
Bibliographic record
Abstract
DESCRIPTION OF CLIENT MANAGEMENT WITH INEFFECTIVE AIRWAYS CLEARANCE IN THE CASE OF PULMONARY TUBERCULOSIS IN YUDISTIRA ROOM RSUD SANJIWANI GIANYAR IN 2021 ABSTRACT Pulmonary tuberculosis is a disease caused by mycrobacterium tuberculosis, these bacteria can infect and cause inflammation of the respiratory tract so that there is a buildup of sputum in the respiratory tract which results in ineffective cleaning of the airway. If this ineffective airway clearance is not managed properly it will cause a lot of harm to the patient. This study aims to determine the description of the client management of ineffective airway cleaning for pulmonary tuberculosis in the Yudistira Room, Sanjiwani Hospital, Gianyar. This type of research is a descriptive research and uses secondary data documentation study data collection techniques. The sample in this study was 33 people from 36 populations. The results showed that there were more male pulmonary tuberculosis patients (63.6%) than female (36.4%). Based on the age of the pulmonary tuberculosis patients, there were more at the age of 44-64 years (45.5%). The nursing action observation component to monitor the AGD value was rarely carried out, namely as much as 12.1%. The therapeutic component of the nursing action performed hyperoxygenation before endotracheal suction and removing the blockage of solid objects with Mcgill forceps was not carried out, namely as much as 0%. The education and collaboration components have been carried out as much as 100%. Based on the results of this study, it can be concluded that the management of the client's airway cleaning is not effective in cases of pulmonary tuberculosis in the Yudistira Room, Sanjiwani Gianyar Hospital has not been carried out as a optimally. Key words: Management, pulmonary tuberculosis, ineffective airways clearance
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 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.016 | 0.002 |
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".