ABSTRACT
Despite the proven safety and clinical efficacy of the Measles vaccine, many countries are seeing new heights of vaccine hesitancy or refusal, and are experiencing a resurgence of measles infections as a consequence. With the use of novel machine learning tools, we investigated the prevailing negative sentiments related to Measles vaccination through an analysis of public Twitter posts over a 5-year period. We extracted original tweets using the search terms related to "measles" and "vaccine," and posted in English from January 1, 2017, to December 15, 2022. Of these, 155,363 tweets were identified to be negative sentiment tweets from unique individuals, through the use of Bidirectional Encoder Representations from Transformers (BERT) Named Entity Recognition and SieBERT, a pretrained sentiment in English analysis model. This was followed by topic modeling and qualitative thematic analysis performed inductively by the study investigators. A total of 11 topics were generated after applying BERTopic. To facilitate a global discussion of results, the topics were grouped into four different themes through iterative thematic analysis. These include (a) the rejection of "anti-vaxxers" or antivaccine sentiments, (b) misbeliefs and misinformation regarding Measles vaccination, (c) negative transference due to COVID-19 related policies, and (d) public reactions to contemporary Measles outbreaks. Theme 1 highlights that the current public discourse may further alienate those who are vaccine hesitant because of the disparaging language often used, while Themes 2 and 3 highlight the typology of misperceptions and misinformation underlying the negative sentiments related to Measles vaccination and the psychological tendency of disconfirmation bias. Nonetheless, the analysis was based solely on Twitter and only tweets in English were included; hence, the findings may not necessarily generalize to non-Western communities. It is important to further understand the thinking and feeling of those who are vaccine hesitant to address the issues at hand.
Subject(s)
COVID-19 , Deep Learning , Social Media , Vaccines , Humans , Vaccination/psychology , AttitudeABSTRACT
BACKGROUND: Risk stratification in paracetamol (acetaminophen) poisoning is crucial because hepatotoxicity is common and can be mitigated with treatment. However, current risk stratification tools have limitations. AIMS: We evaluated the diagnostic performance of the paracetamol concentration × aminotransferase multiplication product, for predicting hepatotoxicity after paracetamol overdose. METHODS: Medline, Cochrane Library and Embase were searched for eligible papers. We used random effects models to obtain pooled estimates of the likelihood ratios and diagnostic odds ratios, from which sensitivity and specificity were computed. We assessed two commonly used cut-off values of paracetamol × aminotransferase, 1500 mg/L × IU/L and 10,000 mg/L × IU/L. Using the confusion matrices of these two cut-offs, area under the summary receiver operator characteristic curve and optimal cut-off values in different clinical scenarios were established. RESULTS: Six studies comprising 5036 participants were included. In 4051 patients, using the cut-off of 1500 mg/L × IU/L, a diagnostic odds ratio of 31.90 (95%CI: 9.52-106.90), sensitivity of 0.98 (95%CI: 0.94-1.00) and specificity of 0.66 (95%CI: 0.49-0.89) were obtained. In 3983 patients, using the cut-off of 10,000 mg/L × IU/L, a diagnostic odds ratio of 99.34 (95%CI: 12.26-804.87), sensitivity of 0.65 (95%CI: 0.51-0.82) and specificity of 0.97 (95%CI: 0.95-1.00) were obtained. For staggered ingestions, the 1500 mg/L × IU/L cut-off yielded a diagnostic odds ratio of 69.53 (95%CI: 4.03-1199.75), sensitivity of 1.00 (95%CI: 0.87-1.00) and specificity of 0.74 (95%CI: 0.43-1.00). Next, using the 10,000 mg/L × IU/L cut-off in this scenario yielded a diagnostic odds ratio of 254.58 (95%CI: 11.12-5827.60), sensitivity of 0.79 (95%CI: 0.59-1.00) and specificity of 0.98 (95%CI: 0.94-1.00). The overall summary receiver operator characteristic curve was 0.91 (95%CI: 0.75-0.97), and the optimal cut-off value was 3840 mg/L × IU/L. The summary receiver operator characteristic curve in patients with staggered ingestions was 0.96 (95%CI: 0.85-0.99). The summary receiver operator characteristic curve in patients with staggered ingestions and whose paracetamol concentration was below the detectable limit of 10 mg/L at presentation was 0.97 (95%CI: 0.94-0.99). CONCLUSION: In this first meta-analysis, paracetamol × aminotransferase demonstrates its use in prognosticating hepatotoxicity in patients with paracetamol poisoning. It complements the Rumack-Matthew nomogram as it has shown promise in addressing two key limitations of the nomogram: it is usable after more than 24 h between overdose and acetylcysteine treatment, and it is applicable in staggered ingestions.
