Maintaining The Status of Hydration on Mr. G With Pneumonia in Fatmawati Hospital
Bibliographic record
Abstract
Pneumonia is a health problem in the world with a high mortality rate both in developing countries and in developed countries such as America, Canada and European countries. In Indonesia, the number three cause of death after cardiovascular disease and tuberculosis. Low socioeconomic factors increase mortality. This infection is generally spread from someone who is exposed in the neighborhood or has direct contact with infected people through their hands or by breathing air (droplets) due to coughing or sneezing. In the pandemic era, pneumonia is very feared because it is related to respiratory diseases which are becoming a trend and issue, so it requires special treatment in this case. This case report aims to improve the provision of nursing care for pneumonia patients to mantaining hydration status with airway clearance problems. Nursing assessment of pneumonia patients was carried out on September 13, 2020, a 52-year-old man who has a history of DM and smoking comes with complaints of coughing but no phlegm, fever since one week ago has disappeared, the patient has diarrhea since 2 days before entering the house pain and stomach pain, the patient also has nausea and vomiting and has no appetite. Nursing intervention for 3 days in accordance with the established outcome criteria. From the nursing intervension carried out, the problem of clearing the airway was resolved, marked by no coughing, normal breathing, no ronchi, moist mucosa and no cyanosis. Nursing intervention to maintaining the patient's adequate hydration status can overcome the problem of ineffective airway clearance.
 
 Keywords: Pneumonia, Airway Clearance, Hydration Status
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".