ArticleGland surgery2023
New parameters measured via preoperative tonsil photos to evaluate the post-tonsillectomy pain: an analysis assisted by machine learning.
Article in Gland surgery, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper, 1 of them a synthesis that pooled it.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
1 citing paper in PubMed, 1 synthesis or guideline pooled it.
- Moving towards the use of artificial intelligence in pain management.European journal of pain (London, England) · 2025Pooled it
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Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Background: Postoperative pain is the most common complication after tonsillectomy. We aimed to explore new parameters related to post-tonsillectomy pain, as well as to construct and validate a model for the preoperative evaluation of patients' risk for postoperative pain. Methods: Data collected from patients who underwent tonsillectomy by the same surgeon at Beijing Chaoyang Hospital from January 2019 to May 2022 were analyzed. Preoperative tonsil images from all patients were taken, and the ratios of the distance between the upper pole of the tonsil and the base of the uvula (L1 for the left side and R1 for the right side) to the width of the uvula (U1) or the length of the uvula (U2) were measured. The following six ratios were calculated: L1/U1, R1/U1, LR1/U1 (the add of L1 and R1, and then divide U1), L1/U2, R1/U2, LR1/U2 (the add of L1 and R1, and then divide U2). The post-tonsillectomy pain was recorded. In addition, machine learning (ML) algorithm and feature importance analysis were used to evaluate the value of the parameters. Results: A total of 100 patients were involved and divided into the training set (60%) and the validation set (40%). All six parameters are negatively correlated with post-tonsillectomy pain. The accuracy, sensitivity, and specificity of the model were 75.0%, 72.7%, and 77.8%, respectively. LR1/U1 and LR1/U2 are the most valuable parameters to evaluate post-tonsillectomy pain. Conclusions: We have discovered new parameters that can be measured using preoperative tonsil images to evaluate post-tonsillectomy pain. ML models based on these parameters could predict whether these patients will have intolerable pain after tonsillectomy and manage it promptly.
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