Faculty Opinions recommendation of Effect of dexamethasone on nausea, vomiting, and pain in paediatric tonsillectomy.
Bibliographic record
Abstract
BACKGROUND: The efficacy of dexamethasone (DEX) to reduce morbidity after paediatric tonsillectomy remains controversial. We evaluated the effect of 0.15 and 0.5 mg kg(-1) DEX on the incidence of postoperative nausea and vomiting (PONV) and on pain intensity after paediatric tonsillectomy.METHODS: A total of 147 children aged 2-8 yr undergoing elective tonsillectomy were included in this prospective randomized double-blind study. At the induction of anaesthesia, subjects received 0.15 mg kg(-1) (DEX 0.15), 0.5 mg kg(-1) (DEX 0.5) DEX, or an equivalent volume of saline solution (placebo). Anaesthetic and surgical techniques were standardized. The incidence of PONV and the need for anti-emetic drugs and additional analgesia (tramadol and/or morphine) were recorded. Postoperative pain was assessed using the Children's Hospital of Eastern Ontario Pain Scale, the visual analogue scale, and the postoperative pain measure for parents.RESULTS: The incidence of early PONV (primary outcome variable) was lower in both DEX groups (DEX 0.15: 21%; DEX 0.5: 22%; placebo: 49%; P=0.001). The incidence of severe pain was reduced in the DEX groups on the second postoperative day (DEX 0.15: 20%; DEX 0.5: 5%; placebo: 47%; P< 0.001). The study was not powered to assess a difference between the two DEX dose groups.CONCLUSIONS: A single i.v. injection of DEX at the induction of anaesthesia was effective in reducing the incidence of early and late PONV and the level of pain on the second postoperative day. A 0.15 mg kg(-1) DEX dose appeared to be as effective as a 0.5 mg kg(-1) dose to reduce the incidence of PONV. PMID: 22879656
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 | 0.004 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.009 | 0.003 |
| Insufficient payload (model declined to judge) | 0.175 | 0.112 |
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".