Essential medical care is a concept referring to indispensable medical care. World Health Organization has defined essential healthcare services as the evidence-based technologies needed to cost-effectively solve health problems. However, most countries have no such term that describes essential healthcare services. Rather, it has been used to efficiently allocate medical resources in countries with limited resources. Therefore, a clear definition of essential healthcare services must be accompanied by a specific purpose and a clear direction for the need. The target to whom medical services are to be provided, the region and institution to be provided, the content of medical services, and the purpose of provision should be distinguished. Recently, as public interest in essential healthcare services has increased, the government announced “the Essential Healthcare Policy Package” and “the 1st Healthcare Reform Implementation Plan” to specifically promote it. But concerns of feasibility and financial estimates are being raised. So, here, I analyze the government policy package and its 1st implantation plan, and suggest policy proposals for them.

Radiofrequency ablation (RFA) is a minimally invasive treatment modality used as an alternative to surgery in patients with benign thyroid nodules, recurrent thyroid cancers (RTCs), and primary thyroid microcarcinomas. The Korean Society of Thyroid Radiology (KSThR) initially developed recommendations for the optimal use of RFA for thyroid tumors in 2009 and revised them in 2012 and 2017. As new meaningful evidence has accumulated since 2017 and in response to a growing global interest in the use of RFA for treating malignant thyroid lesions, the task force committee members of the KSThR decided to update the guidelines on the use of RFA for the management of RTCs based on a comprehensive analysis of current literature and expert consensus.

Objective: A humanities-based approach to understanding the brain can yield valuable insights, advancing neuroscience and enhancing mental, emotional, and social well-being. This study was aimed at exploring how engagement in literature-related activities stimulates brain activity in the prefrontal cortex. Methods: We recruited 24 healthy male participants aged 20 to 29 years. They completed clinical scales assessing depression, anxiety, attention, and humanistic knowledge. They also performed six tasks comprising various literature-related cognitive challenges while hemodynamic changes in their frontal cortices were measured using functional near-infrared spectroscopy. Results: Task 1 (word memory and recognition) increased activation in the ventrolateral prefrontal cortex (VLPFC), as did Task 2 (emotional words classification), which also elevated activity in the left orbitofrontal cortex (OFC). Task 3 (understanding context) increased activation in the dorsolateral PFC (DLPFC). Tasks 4 (interpersonal relationship) and 5 (listening, memory, understanding, and expression) drove similar increases in the frontopolar and DLPFC regions. Task 6 (creative activities using characters and items) significantly activated multiple regions, including the right and left VLPFC and OFC. Humanistic knowledge scores were positively correlated with left and right DLPFC activation in Tasks 3 and 5, respectively. Conversely, Task 6 showed negative correlations between attention-deficit/hyperactivity disorder scores and both right DLPFC and right OFC activation. Conclusion This study identified key brain regions involved in literature activities. Complex activities (semantic processing, understanding and creative expression, decision-making and emotional regulation, etc.) stimulated various regions of prefrontal cortices, including the VLPFC, DLPFC, and OFC.

Objective: Extraction of the mandibular second molar (L7) and substitution by the mandibular third molar (L8) is an effective treatment option. This study aimed to evaluate spontaneously erupted L8 occlusion after L7 extraction, and identify the influencing factors. Methods: This study assessed 46 L8 from 28 patients using dental study models, panoramic radiographs, and lateral cephalograms obtained during L7 extraction (T1) and completion of L8 eruption (T2). At T2, samples were categorized as acceptable (A-group) or unacceptable (U-group) based on the American Board of Orthodontics index.L8 angulation and position, retromolar space, distance between the Xi point and mandibular first molar (L6), and Nolla stage were compared between the groups to identify the predictive factors for successful eruption. Results: At T2, 58.7% of L8 exhibited acceptable occlusion. Age at T1 was significantly higher in the U-group than that in the A-group. Angles ∠6-MnP and ∠8-MnP differed significantly between the groups at T2. Xi-L6 distance was considerably longer in the A-group than that in the U-group at T1 and T2. Younger age at extraction and Xi-L6 distance at T1 affected the acceptable occlusion. Conclusions Younger age at L7 extraction and adequate eruption space (Xi-L6 distance) appear to be the key factors for achieving acceptable L8 occlusion.

Purpose: This study aimed to validate pivotal pre-test probability (PTP)-coronary artery disease (CAD) models (CAD consortium model and IJC-CAD model). Materials and Methods: Traditional PTP models-CAD consortium models: two traditional PTP models were used under the CAD consortium framework, namely CAD1 and CAD2. Machine learning (ML)-based PTP models: two ML-based PTP models were derived from CAD1 and CAD2, and used to enhance predictive capabilities [ML-CAD2 and ML-IJC (IJC-CAD)]. The primary endpoint was obstructive CAD. The performance evaluation of these PTP models was conducted using receiver-operating characteristic analysis. Results: The study included 238 participants, among whom 157 individuals (65.9% of the total sample) had CAD. The IJC-CAD model demonstrated the highest performance with an area under the curve (AUC) of 0.860 [95% confidence interval (CI): 0.812– 0.909]. Following this, the ML-CAD2 model exhibited an AUC of 0.814 (95% CI: 0.758–0.870), CAD1 showed an AUC of 0.767 (95% CI: 0.705–0.830), and CAD2 had an AUC of 0.785 (95% CI: 0.726–0.845). Each of the PTP models was adjusted to have a CAD score cutoff that classified cases with a sensitivity of over 95%. The respective cutoff values were as follows: CAD1 and CAD2 >12, MLCAD2 >0.380, and IJC-CAD >0.367. All PTP models achieved a CAD sensitivity of over 95%. Similar to the AUC performance, the accuracy of the PTP models was highest for IJC-CAD, reaching 80.3%. The accuracy of ML-CAD2 was 77.7%, while that for CAD1 and CAD2 was 74.8% and 75.2%, respectively. Conclusion ML-CAD2 and IJC-CAD showed superior performance compared to traditional existing models (CAD1 and CAD2)

Purpose: Early identification of patients at risk for acute respiratory failure (ARF) could help clinicians devise preventive strategies. Analyzing biosignals with artificial intelligence (AI) can uncover hidden information and variability within time series. We aimed to develop and validate AI models to predict ARF within 72 h after emergency department admission, primarily using highresolution biosignals collected within 4 h of arrival. Materials and Methods: Our AI model, built on convolutional recurrent neural networks, combines biosignal feature extraction and sequence modeling. The model was developed and internally validated with data from 5284 admissions [1085 (20.5%) positive for ARF], and externally validated using data from 144 admissions [7 (4.9%) positive for ARF] from another institution. We defined ARF as the application of advanced respiratory support devices. Results: Our AI model performed well in predicting ARF, achieving area under the receiver operating characteristic curve (AUROC) of 0.840 and 0.743 in internal and external validations, respectively. It outperformed the Modified Early Warning Score (MEWS) and XGBoost models built only with clinical variables. High predictive ability for mortality was observed, with AUROC up to 0.809. A 10% increase in AI prediction scores was associated with 1.44-fold and 1.42-fold increases in ARF risk and mortality risk, respectively, even after adjusting for MEWS and demographic variables. Conclusion Our AI model demonstrates high predictive accuracy and significant associations with clinical outcomes. Our AI model has the potential to promptly aid in triage decisions. Our study shows that using AI to analyze biosignals advances disease detection and prediction.

Pneumocystis jirovecii pneumonia (PJP) is a life-threatening infection commonly observed in immunocompromised patients, necessitating prompt diagnosis and treatment. This review evaluates the diagnostic performance of various tests used for PJP diagnosis through a comprehensive literature review. Additionally, we propose a diagnostic algorithm tailored to non-human immunodeficiency virus immunocompromised patients, considering the specific characteristics of current medical resources in Korea.

Background: The treatment of Kümmell disease (KD) is controversial. Corpectomy and reconstruction or osteotomy with long-level fusion was traditionally performed for the advanced KD. However, these procedures can be disadvantageous for elderly patients.Several alternative surgical procedures including transpedicular intravertebral cage augmentation (TPICA) or vertebroplasty (VP) combined with short-segment fixation (SSF) have been suggested to minimize the surgical burden. This study aimed to compare the outcomes of percutaneous TPICA plus SSF with VP plus SSF for advanced thoracolumbar (T11–L2) KD and to introduce our novel percutaneous TPICA technique using specially designed cannulated cage trials. Methods: We devised specially designed cannulated cage trials to make the TPICA procedure safer and more reproducible, minimizing the risk of the pedicle medial wall violation. All consecutive patients who underwent percutaneous TPICA or VP combined with SSF for advanced thoracolumbar KD, from January 2021 to June 2022, with ≥ 1-year follow-up at a single institution, were included. Perioperative details, clinical outcomes (visual analog scale and Oswestry Disability Index), and radiological outcomes (anterior vertebral body compression percentage and vertebral kyphotic angle [VKA] of the fractured vertebra, and local Cobb angle [LCA]) were collected and compared between the groups. Results: A total of 42 patients were enrolled, with 21 patients in each group. There were no patients with pedicle medial wall fracture in the TPICA group. Both procedures provided significantly favorable radiological outcomes compared to those preoperatively. No significant differences were observed in the changes over time in all radiological parameters between the groups. Loss of correction during the follow-up period was significantly smaller in patients with TPICA than in those with VP in VKA (median [interquartile range], 2.15 [0.30–2.80] vs. 2.90 [0.90–6.53]; p = 0.030) and LCA (2.70 ± 2.90 vs. 5.17 ± 4.40, p = 0.037). Conclusions Both procedures are minimally invasive and useful options for advanced KD, especially for elderly patients with high comorbidity. Our novel percutaneous TPICA technique using cannulated cage trials, being safer and more reproducible, may allow spine surgeons to easily perform TPICA.

We evaluated the effectiveness of the predictive low-glucose suspend (PLGS) algorithm in the DIA:CONN G8. Forty people with type 1 diabetes mellitus (T1DM) who used a DIA:CONN G8 for at least 2 months with prior experience using pumps without and with PLGS were retrospectively analyzed. The objective was to assess the changes in time spent in hypoglycemia (percent of time below range [%TBR]) before and after using PLGS. The mean age, sensor glucose levels, glucose threshold for suspension, and suspension time were 31.1±22.8 years, 159.7±23.2 mg/dL, 81.1±9.1 mg/dL, and 111.9±79.8 min/day, respectively. Overnight %TBR <70 mg/dL was significantly reduced after using the algorithm (differences=0.3%, from 1.4%±1.5% to 1.1%±1.2%, P=0.045). The glycemia risk index (GRI) improved significantly by 4.2 (from 38.8±20.9 to 34.6±19.0, P=0.002). Using the PLGS did not result in a change in the hyperglycemia metric (all P>0.05). Our findings support the PLGS in DIA:CONN G8 as an effective algorithm to improve night-time hypoglycemia and GRI in people with T1DM.

Background: Active surveillance (AS) has emerged as a viable management strategy for low-risk papillary thyroid microcarcinoma (PTMC), following pioneering trials at Kuma Hospital and the Cancer Institute Hospital in Japan. Numerous prospective cohort studies have since validated AS as a management option for low-risk PTMC, leading to its inclusion in thyroid cancer guidelines across various countries. From 2016 to 2020, the Multicenter Prospective Cohort Study of Active Surveillance on Papillary Thyroid Microcarcinoma (MAeSTro) enrolled 1,177 patients, providing comprehensive data on PTMC progression, sonographic predictors of progression, quality of life, surgical outcomes, and cost-effectiveness when comparing AS to immediate surgery. The second phase of MAeSTro (MAeSTro-EXP) expands AS to low-risk papillary thyroid carcinoma (PTC) tumors larger than 1 cm, driven by the hypothesis that overall risk assessment outweighs absolute tumor size in surgical decision-making. Methods: This protocol aims to address whether limiting AS to tumors smaller than 1 cm may result in unnecessary surgeries for low-risk PTCs detected during their rapid initial growth phase. By expanding the AS criteria to include tumors up to 1.5 cm, while simultaneously refining and standardizing the criteria for risk assessment and disease progression, we aim to minimize overtreatment and maintain rigorous monitoring to improve patient outcomes. Conclusion This study will contribute to optimizing AS guidelines and enhance our understanding of the natural course and appropriate management of low-risk PTCs. Additionally, MAeSTro-EXP involves a multinational collaboration between South Korea and Australia. This cross-country study aims to identify cultural and racial differences in the management of low-risk PTC, thereby enriching the global understanding of AS practices and their applicability across diverse populations.