This study aimed to investigate whether early surgical decompression was associated with favorable neurological recovery in patients with traumatic spinal cord injury (tSCI). We searched PubMed and Embase from the database inception through December 2020 and selected studies comparing the impact of early versus late surgical decompression on neurological recovery as assessed by American Spinal Injury Association Impairment Scale (AIS) for adult patients sustaining tSCI. We pooled the effect estimates in random-effects models and quantified the heterogeneity by the I ² statistics. Subgroup analysis and meta-regression analysis was conducted to identify significant outcome moderator. We included 26 studies involving 3574 patients in the meta-analysis. The pooled results demonstrated significant association between early surgical decompression and an improvement of at least one AIS grade (odds ratio [OR], 1.85; 95% confidence interval [CI], 1.41-2.41; I ² , 48.06%). The benefits of early surgical decompression were consistently observed across different subgroups, including patients with cervical or thoracolumbar injury and patients with complete or incomplete injury. The meta-regression analysis indicated that cut-off timing defining early versus late decompression was a significant effect moderator, with early decompression performed before post-tSCI 8 or 12 h associated with greatest benefits (OR, 3.37; 95% CI, 1.74-6.50; I ² , 53.52%). No obvious publication bias was detected by the funnel plot. In conclusion, early surgical decompression was associated with favorable neurological recovery for tSCI patients. However, there was a lack of high-quality evidence and the results need further examination.

Left without being seen (LWBS) is a quality care metric associated with patient-centered outcomes. The risks affecting LWBS are complex and interventions targeting certain risks have diverse effects. We aimed to use different artificial intelligence and machine learning (AI/ML) algorithms to identify the risks affecting LWBS, implement triple interventions specifically targeted at such risks, and compare daily LWBS rate changes before and after the intervention. This is a retrospective observational study. Single urban Emergency Department (ED) daily throughput data from March 1, 2019, to February 28, 2023, were used for AI/ML model prediction. Model performance including accuracy, recall, precision, F1 score, and area under the receiver operating characteristics (AUC) were reported. The top risks affecting the LWBS were identified using the important function of the AI/ML feature. Triple interventions were implemented. The average daily LWBS rate was compared before (March 1, 2019, to February 28, 2023) and after (June 1, 2023, to May 31, 2024). A total of 1919 daily throughput metrics were analyzed, including 1461 daily metrics before the intervention, 92 daily metrics during the wash period, and 366 daily metrics after the intervention. Using data before the intervention, the Extreme Gradient Boosting (XGBoost) and Random Forest AI/ML algorithms predicted LWBS with a similar favorable performance. The 3 common factors influencing the increased daily LWBS rate were triage-to-bed (wait time), boarding time, and door-to-triage in the ED. Rapid triage, direct bedding, and boarding reduction (triple intervention) were implemented on March 1, 2023. We found 4.82% of daily LWBS before the triple intervention compared to 1.93% of daily LWBS after the triple intervention (P < .001). AI/ML approaches can identify common factors that are highly related to LWBS with favorable performance. Triple interventions targeting these factors can reduce the daily LWBS rate by approximately 60%, indicating the efficiency of the ED operational management.

Quality care in healthcare is a multifaceted concept that encompasses the execution of effective medical treatments and the patient’s overall experience. It involves a multitude of factors, including effectiveness, safety, timeliness, equity, and patient centeredness, which are important in shaping the healthcare landscape. This cross-sectional study used the data from the Health Information National Trends Survey 6 (HINTS 6), which collects data on various aspects of health communication and information-seeking behaviors, to investigate the factors associated with quality care among White and Hispanic populations. All adults who participated in HINTS 6 and visited healthcare service at least once in the past 12 months were included in this study. Multivariable logistic regression was used to determine the association between quality care and delay or discriminated care with the adjustment of all other sociodemographic variables. We analyzed a total of 3611 participants. Poor social determinants of health (SDOHs) (OR 0.61, CI 0.43–0.88, p = 0.008), delayed needed medical care (OR 0.34, CI 0.26–0.43, p < 0.001), and discriminated care (OR 0.29, CI 0.15–0.54, p < 0.001) were all negatively associated with optimal quality care. Negative SDOHs could also be positively associated with delayed care and discriminated care.

Objectives: The misuse of pain relievers has been linked to mental and behavioral disorders. This study aims to determine the associations between pain-reliever misuse, severe psychological distress (SPD), suicidal ideation, and difficulties in performing daily activities. Additionally, it seeks to identify the socio-demographic factors associated with pain-reliever misuse across different racial and ethnic groups. Methods: This cross-sectional study utilizes data from the 2022 United States National Survey on Drug Use and Health (NSDUH). Participants were categorized into four groups: non-Hispanic White (NHW), non-Hispanic Black (NHB), Hispanic/Latino (Hispanic), and Other (American Indian, Alaska Native, Asian, Native Hawaiian or other Pacific Islanders, and two or more races) groups. Comparisons were made between individuals regarding pain-reliever misuse, socio-demographic characteristics, SPD, suicidal thoughts, and World Health Organization Disability Assessment Schedule (WHODAS) scores, using Rao–Scott Chi-square tests. Stepwise multivariable logistic regression analyses were conducted to identify socio-demographic factors associated with pain-reliever misuse. Results: The study included 45,451 participants, with 27,551 (62.00 wt%) identified as NHW, 5186 (11.98 wt%) as NHB, 7795 (17.15 wt%) as Hispanic, and 4919 (8.87 wt%) as other racial and ethnic groups. The rate of pain-reliever misuse was 2.90% among NHWs, 3.40% among NHBs, 3.61% among Hispanics, and 2.05% among individuals of other races and ethnicities (p = 0.043). Among those who misused pain relievers, a significantly higher proportion experienced SPD (36.00% vs. 14.05%), suicidal thoughts (15.51% vs. 4.91%), and difficulties in performing daily activities (73.77% vs. 52.84%) compared to those who did not misuse pain relievers (p < 0.001). Socio-demographic factors associated with a lower risk of misuse included being female (AOR = 0.80, 95% CI 0.67–0.95, p = 0.013), being employed (AOR = 0.66, 95% CI 0.48–0.90, p = 0.010), and having a college or higher education (AOR = 0.54, 95% CI 0.37–0.79, p = 0.002). Conclusions: The prevalence of pain-reliever misuse varies across racial and ethnic groups, with Hispanic individuals demonstrating the highest rates of misuse. Pain-reliever misuse is strongly associated with SPD, suicidal thoughts, and impaired daily functioning. Socio-demographic factors are crucial in predicting the likelihood of pain-reliever misuse. These findings highlight the importance of culturally tailored prevention strategies and public health policies aimed at mitigating misuse, especially among vulnerable populations.