Effect of Cryotherapy plus Flurbiprofen Axetil for Pain Management in Children Undergoing Tonsillectomy
Post-publication record
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Bibliographic record
Abstract
Objective. To investigate the effect of cryotherapy using ice pops for physical analgesia and preventive analgesia using flurbiprofen axetil for pain management in children undergoing tonsillectomy. Methods. A total of 120 children scheduled for tonsillectomy were recruited after assessment for eligibility and assigned to a control group (group C), flurbiprofen axetil group (group F), cryotherapy group (group I), and cryotherapy plus flurbiprofen axetil group (Group FI) via the random number table method. Groups F and FI were given 1 mg/kg of flurbiprofen axetil through intravenous injection 30 min before surgery, while group C received an equal amount of saline at the same time point. Groups I and FI received sweet ice pops for pain relief after recovery from anesthesia. The modified Children’s Hospital of Eastern Ontario Pain Scale (mCHEOPS) scores and pediatric anesthesia emergence delirium (PAED) scores at 5 minutes (T1), 30 minutes (T2), 60 minutes (T3), 4 hours (T4), and 24 hours (T5) postoperatively, and the incidence of postoperative complications in the children were recorded by investigators who were masked to the grouping results. Results. From T1 to T4, significantly lower mCHEOPS scores and PAED scores were observed in group F, group I, and group FI versus those in group C ( P < 0.05 ). At T2, group FI showed significantly lower mCHEOPS scores and PAED scores versus groups F and I ( P < 0.05 ). There were no significant differences in the mCHEOPS scores and PAED scores between the four groups at 24 h postoperatively ( P > 0.05 ). The differences in the documented postoperative complications between the four groups did not come up to the statistical standard ( P > 0.05 ). Conclusion. Cryotherapy plus flurbiprofen axetil for pain management significantly mitigates post-tonsillectomy pain and delirium in children and facilitates recovery, with no significant adverse events.
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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) | 0.001 | 0.001 |
| 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".