Methods: Using a 2-by-2 factorial design, 12 705 participants from 21 countries with vascular risk factors but without overt cardiovascular disease were randomized to candesartan 16 mg plus hydrochlorothiazide 12.5 mg daily or placebo and to rosuvastatin 10 mg daily or placebo. The effect of the interventions on stroke subtypes was assessed.
Results: Participants were 66 years old and 46% were women. Baseline blood pressure (138/82 mm Hg) was reduced by 6.0/3.0 mm Hg and LDL-C (low-density lipoprotein cholesterol; 3.3 mmol/L) was reduced by 0.90 mmol/L on active treatment. During 5.6 years of follow-up, 169 strokes occurred (117 ischemic, 29 hemorrhagic, 23 undetermined). Blood pressure lowering did not significantly reduce stroke (hazard ratio [HR], 0.80 [95% CI, 0.59–1.08]), ischemic stroke (HR, 0.80 [95% CI, 0.55–1.15]), hemorrhagic stroke (HR, 0.71 [95% CI, 0.34–1.48]), or strokes of undetermined origin (HR, 0.92 [95% CI, 0.41–2.08]). Rosuvastatin significantly reduced strokes (HR, 0.70 [95% CI, 0.52–0.95]), with reductions mainly in ischemic stroke (HR, 0.53 [95% CI, 0.37–0.78]) but did not significantly affect hemorrhagic (HR, 1.22 [95% CI, 0.59–2.54]) or strokes of undetermined origin (HR, 1.29 [95% CI, 0.57–2.95]). The combination of both interventions compared with double placebo substantially and significantly reduced strokes (HR, 0.56 [95% CI, 0.36–0.87]) and ischemic strokes (HR, 0.41 [95% CI, 0.23–0.72]).
Conclusions: Among people at intermediate cardiovascular risk but without overt cardiovascular disease, rosuvastatin 10 mg daily significantly reduced first stroke. Blood pressure lowering combined with rosuvastatin reduced ischemic stroke by 59%. Both therapies are safe and generally well tolerated.
Registration: URL: https://www.clinicaltrials.gov; Unique identifier: NCT00468923.
DESIGN: Prospective, randomised, within-subject cross-over trial.
SETTING: Single-centre, tertiary, university hospital in Malaysia.
PARTICIPANTS: 72 women within 24-hour of first admission for HG who were 18 years or above, with confirmed clinical pregnancy of less than 16 weeks' gestation were recruited and analysed. Women unable to consume food due to extreme symptoms, known taste or swallowing disorder were excluded.
INTERVENTIONS: Each participant chewed and swallowed a small piece of apple, watermelon, cream cracker and white bread in random order and was observed for 10 min after each tasting followed by a 2 min washout for mouth rinsing and data collection.
OUTCOME MEASURES: Primary outcome was food agreeability scored after 10 min using an 11-point 0-10 Visual Numerical Rating Scale (VNRS). Nausea was scored at baseline (prior to tasting) and 2 and 10 min using an 11-point VNRS. Intolerant responses of gagging, heaving and vomiting were recorded.
RESULTS: On agreeability scoring, apple (mean±SD 7.2±2.4) ranked highest followed by watermelon (7.0±2.7) and crackers (6.5±2.6), with white bread ranked lowest (6.0±2.7); Kruskal-Wallis H test, p=0.019. Apple had the lowest mean nausea score and mean rank score, while white bread had the highest at both 2 and 10 min; the Kruskal-Wallis H test showed a significant difference only at 10 min (p=0.019) but not at 2 min (p=0.29) in the ranking analyses. The intolerant (gagged, heaved or vomited) response rates within the 10 min study period were apple 3/72 (4%), watermelon 7/72 (10%), crackers 8/72 (11%) and white bread 12/72 (17%): χ2 test for trend p=0.02.
CONCLUSION: Sweet apple had the highest agreeability score, the lowest nausea severity and intolerance-emesis response rate when tasted by women with HG. White bread consistently performed worst.
Method: Scopus, PubMed, and Web of Science (all databases) were searched by 2 reviewers until 29th October 2020. Articles were screened and narratively synthesized according to PRISMA-DTA guidelines based on predefined eligibility criteria. Articles that made direct reference test comparisons to human clinicians were evaluated using the MI-CLAIM checklist. The risk of bias was assessed by JBI-DTA critical appraisal, and certainty of the evidence was evaluated using the GRADE approach. Information regarding the quantification method of dental pain and disease, the conditional characteristics of both training and test data cohort in the machine learning, diagnostic outcomes, and diagnostic test comparisons with clinicians, where applicable, were extracted.
Results: 34 eligible articles were found for data synthesis, of which 8 articles made direct reference comparisons to human clinicians. 7 papers scored over 13 (out of the evaluated 15 points) in the MI-CLAIM approach with all papers scoring 5+ (out of 7) in JBI-DTA appraisals. GRADE approach revealed serious risks of bias and inconsistencies with most studies containing more positive cases than their true prevalence in order to facilitate machine learning. Patient-perceived symptoms and clinical history were generally found to be less reliable than radiographs or histology for training accurate machine learning models. A low agreement level between clinicians training the models was suggested to have a negative impact on the prediction accuracy. Reference comparisons found nonspecialized clinicians with less than 3 years of experience to be disadvantaged against trained models.
Conclusion: Machine learning in dental and orofacial healthcare has shown respectable results in diagnosing diseases with symptomatic pain and with improved future iterations and can be used as a diagnostic aid in the clinics. The current review did not internally analyze the machine learning models and their respective algorithms, nor consider the confounding variables and factors responsible for shaping the orofacial disorders responsible for eliciting pain.