METHODS: This was a prospective non-randomized comparative study. Eyes with OAG and cataracts that were planned for either combined phacoemulsification and iStent implantation (iStent+CS) or phacoemulsification alone (CS) were recruited. The iStent inject (Model G2-M-IS) or iStent injectW (Model G2-W) trabecular micro-bypass stent (Glaukos Corporation, San Clemente, CA, USA) was implanted in the iStent+CS group. WDT was performed before and 3 months after surgery. WDT-IOP parameters including peak IOP, IOP fluctuation, and area under the curve (AUC) were compared between the two groups.
RESULTS: There were 20 eyes in the iStent+CS group and 16 eyes in the CS group. Both groups had similar pre-operative baseline IOP (15.6 ± 3.7 mm Hg vs. 15.8 ± 1.8 mm Hg in the iStent+CS and CS group, respectively, p = 0.883). The iStent+CS group experienced greater numerical reduction in peak IOP (2.6 ± 1.9 mm Hg vs. 1.9 ± 2.4 mm Hg; p = 0.355), IOP fluctuation (1.7 ± 2.2 mm Hg vs. 0.8 ± 2.5 mm Hg; p = 0.289), and AUC (54.8 ± 103.6 mm Hg × minute vs. 25.3 ± 79.0 mm Hg × minute; p = 0.355) than the CS group. There was more reduction in the number of anti-glaucoma medications in the iStent+CS group (1.4 ± 1.2) than the CS group (0.3 ± 0.9; p = 0.005).
CONCLUSION: Both combined phacoemulsification with iStent inject implantation and phacoemulsification alone reduced peak IOP, IOP fluctuation, and AUC, and none of these parameters showed statistically significant difference. Greater reduction in anti-glaucoma medications was seen in the combined group.
OBJECTIVES: By leveraging the power of advanced machine learning schemes and experimental approaches, this research aims to provide valuable insights into CO2 flux prediction in coal fire areas and inform environmental monitoring and management strategies.
METHODS: The study involves the collection of an experimental dataset specific to underground coal fire areas, encompassing various parameters related to CO2 flux and underground coal fire characteristics. Innovative feature engineering techniques are applied to capture the unique characteristics of underground coal fire areas and their impact on CO2 flux. Different machine learning algorithms, including Natural gradient boosting regression (NGRB), Extreme gradient boosting (XGboost), Light gradient boosting (LGRB), and random forest (RF), are evaluated and compared for their predictive capabilities. The models are trained, optimized, and assessed using appropriate performance metrics.
RESULTS: The NGRB model yields the best predictive performances with R2 of 0.967 and MAE of 0.234. The novel contributions of this study include the development of accurate prediction models tailored to underground coal fire areas, shedding light on the underlying factors driving CO2 flux. The findings have practical implications for delineating the spontaneous combustion zone and mitigating CO2 emissions from underground coal fires, contributing to global efforts in combating climate change.
METHODS: A retrospective analysis of prospectively collected data was conducted. Skeletal maturity was determined using Risser, SSMS, TOCI and CVM for each patient. Crosstabulations of axial vs. appendicular markers were formed to analyze their concordance and discordance. Logistic and logarithmic regression models were run to assess longitudinal growth (postoperative height gain and leg-length growth) and curve modulation (follow-up instrumented Cobb correction after index operation), respectively. Models were compared using Akaike information criterion (AIC).
RESULTS: 34 patients (32 F/2 M, mean age: 12.8 ± 1.5 years, mean follow-up: 47.7 (24-80) months) were included. The median preoperative maturity stages were: Risser: 1 (-1-4), SSMS: 4 (1-7), TOCI: 6 (1-8) and CVM: 4 (1-6). At latest follow-up, all patients reached skeletal maturity. Concordance and discordance were observed between axial vs. appendicular systems that demonstrated a range of possible distributions of CVM, where trunk peak height velocity occurred before, simultaneously with or after the standing height peak height velocity. R-squared values for Risser, SSMS, TOCI and CVM were 0.701, 0.783, 0.810 and 0.811, respectively, for prediction of final height; 0.759, 0.821, 0.831 and 0.775 for final leg-length, and 0.507, 0.588, 0.668 and 0.673 for curve modulation. Delta AIC values demonstrated that different skeletal maturity assessment methods provided distinctive information regarding follow-up height gain, leg-length growth and curve behavior.
CONCLUSIONS: Risser score provided considerably less information for all three outcome variables. TOCI and SSMS provided substantial information regarding remaining leg-length assessments, while in terms of assessment of total height gain and curve modulation after surgery, CVM and TOCI offered substantial information and SSMS offered strong information. Mutual use of axial and appendicular markers may provide valuable insight concerning timing of surgery and magnitude of surgical correction.
METHODS: This study involved a modified electronic Delphi technique involving 27 specialists working in primary care recruited via convenient and snowball sampling. The Delphi survey was conducted online between August 2022 and April 2023, utilizing the Google Forms platform. Descriptive statistics were employed to analyse consensus across Delphi rounds.
RESULTS: Twenty-six international experts participated in the survey. The retention rate through the second and third Delphi rounds was 96.2% (n = 25). The broader consensus definition emphasizes person-centred care, collaborative patient-physician partnerships, and a holistic approach to health, including managing acute and chronic conditions through in-person or remote access based on patient preferences, medical needs, and local health system organization.
CONCLUSION: The study highlights the importance of continuity of care, prevention, and coordination with other healthcare professionals as core values of primary care. It also reflects the role of GP/FM in addressing new challenges post-pandemic, such as healthcare delivery beyond standard face-to-face care (e.g. remote consultations) and an increasingly important role in the prevention of infectious diseases. This underscores the need for ongoing research and patient involvement to continually refine and improve primary healthcare delivery in response to changing healthcare landscapes.