A Literature Review of Factors Related to Postoperative Sore Throat
Bibliographic record
Abstract
Postoperative sore throat can occur as a complication in patients who have undergone surgery under general anesthesia. The incidence of postoperative sore throat ranges from 12.1% to 70%, and its effects include damage to the epithelium and mucosal cells caused by airway securement, damage to the vocal cords, congestion, blood clots, and factors such as an inappropriately large tube, cuff shape, cuff pressure, and airway securement. Notably, there are individual differences in pain thresholds, and the sensation of pain is affected by mental states, such as anxiety, and varies from person to person. Therefore, we conducted a literature review using PubMed to clarify patient factors related to the development of postoperative sore throat. The extracted keywords were "postoperative sore throat," "anesthesia," and "patient factors." We found 16 articles that met our search criteria. We expanded the search period and retrieved 19 cases from 1990 to 2020. We also included references that were judged to be closely related to the list of citations of the retrieved references. The study designs included were randomized controlled trials, clinical trials, meta-analyses, reviews, and systematic reviews. The results showed that female sex, smoking, and age were the most common patient factors. However, we could not find any literature that studied the relationship between postoperative sore throat and mental states such as anxiety.
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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 | 0.003 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.004 |
| Bibliometrics | 0.015 | 0.017 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".