The Effect of Nebulized Salbutamol on Serum Potassium and Blood Sugar Level of Asthmatic Patients
Bibliographic record
Abstract
BACKGROUND: Nebulized Salbutamol have great advantages for patients with respiratory problems by depositing drugs directly to the lungs, inspite of reported adverse metabolic effects on different electrolytes and glucose heamostasis of patients.AIM OF STUDY: To evaluate the effect of nebulized salbutamol used in the management of patients with asthma who have normal serum potassium and blood glucose levels. in the emergency department after 30 and 60 minutes of administration and to find out if these results are of clinical importance that should be taken in consideration when treating patients especially those with abnormal glucose hemostasis or electrolyte disturbance. PATIENTS & METHODS: The study is a prospective follow up study conducted in Emergency Department of Baghdad Teaching Hospital through the period from 1st of April, 2017, to 31st of January, 2018 on a sample of 100 patients. After administration of nebulized salbutamol, the Potassium and Glucose levels of patients were assessed in three periods; baseline, after 30 minutes and after one hour.RESULTS: The potassium mean was significantly decreased after 1 hour of nebulizer administration (p<0.001). The random blood sugar mean was significantly increased after 1 hour of nebulizer administration (p<0.001). The potassium level was significantly decreased one hour after nebulizer administration for patients with negative steroids history (p=0.03), while no significant difference in potassium level was observed for patients with positive steroids history.CONCLUSIONS: The nebulizer applying salbutamol has a profound effect in lowering the Potassium level and increasing blood glucose level after 60 minutes of administration.
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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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".