Summary of the best evidence for pain intervention in patients undergoing tonsil surgery
Bibliographic record
Abstract
ObjectiveTo summarize the best evidence of postoperative pain intervention for patients undergoing tonsil surgery.MethodsAll evidence related to tonsil postoperative pain intervention was systematically retrieved from BMJ Best Clinical Practice，JBI Evidence⁃Based Health Care Center Database，the Registered Nurses' Association of Ontario（RNAO） Network，the Scottish Intercollegiate Guidelines Network（SIGN），Yimaitong Network，Cochrane Library，PubMed，UpToDate，Chinese Biomedical Literature Database，Wanfang Database，and China National Knowledge Infrastructure，including guidelines，expert consensus，evidence summary，systematic reviews，and meta⁃analysis.The search time limit was from the establishment of the database to April 30，2020.Two researchers independently completed the quality evaluation of the included literature，then extracted and summarized the evidence that meets the quality standards combined with the judgment of professionals.ResultsA total of 19 articles were included，involving 1 guideline and 18 systematic reviews or meta⁃analysis，which summarized 18 pieces of evidence concerning postoperative pain interventions for patients undergoing tonsillectomy，including drugs and non⁃drugs.Among the evidences，11 pieces were given grade A recommendation and 7 pieces with grade B recommendation；intervention methods involved drugs，local anesthesia，psychological guidance，pain management，cold liquid diet，oral honey，ice compress，and distraction.ConclusionsWhen applying perioperative pain intervention evidence for patients undergoing tonsil surgery，the clinical situation should be concerned，and the best evidence should be selected to reduce the degree of postoperative 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 | 0.013 | 0.067 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.009 | 0.011 |
| Bibliometrics | 0.015 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.014 | 0.001 |
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".