Nursing Process in Post Tonsillectomy Pain Diagnosis: A Systematic Review
Bibliographic record
Abstract
OBJECTIVE: Tonsillectomy is the most common surgery in the field of ENT. Pain is the most common post tonsillectomy complaint. Considering the importance of nursing cares in relieving post-surgery pain in general and post-tonsillectomy pain in particular, this study is conducted with the aim of presenting nursing process in post tonsillectomy pain diagnosis for decreasing loss of appropriate opportunities in nursing cares and achieving appropriate results in taking care of the patients. METHODS: This study is a targeted systematic review focusing on "effective nursing measures in relieving children's post tonsillectomy pain". The main stages of searching strategy included searching in electronic sources of Latin databases; Pub Med, Science Direct, and EMBASE and Persian databases; SID, Iran medex, ISC to find published articles from 2009 to 2014. In the end, final synthesis was done on eight articles in English. FINDINGS: Effective nursing measurements for relieving post tonsillectomy pain include: decreasing children's anxiety through children and their families' psychological preparation by nurses and other caregivers, using cold compress to reduce neck and jaw pain, presenting distraction techniques, offering fluids and cold foods immediately in the period after surgery, creating a comfortable environment for the children, avoiding too much of talking and adequate sleep. CONCLUSION: It is recommended to the nursing managers and nurses to perform cares achieved from this systematic review to achieve appropriate results in relieving post tonsillectomy pain.
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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 | 0.007 | 0.033 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.006 |
| Bibliometrics | 0.007 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".