The Italian Treasury Econometric Model (ITEM)

In this paper, we provide a description of the Italian Treasury Econometric Model (ITEM). We illustrate its general structure and model properties, especially with regard to the economy's response to changes in policy and in other dimensions of the economic environment. The model has a quarterly frequency and includes 371 variables. Out of these, 124 are exogenous and 247 endogenous. The model structure features 36 behavioral equations and 211 identities. One of the key features of the model is the joint representation of the economic environment on both the demand and the supply side. Since it is designed for the needs of a Treasury Department, its public finance section is developed in great detail, both on the expenditure and revenue side. It also features a complete modeling of financial assets and liabilities of each institutional sector. After documenting the model structure and the estimation results, we turn to the outcomes of model simulation and ascertain the model properties. In ITEM the shocks that generate permanent effects on output are associated with: a) variation of variables that affect the tax wedge in the labor market and the user cost of capital; b) labor supply change; c) variation in the trend component of TFP (technical progress). By contrast, variables that exert their effects on the demand side have only temporary effects on output. We also perform in-sample dynamic simulation of the model. This allows us to derive simulated values of all the endogenous variables which can be compared with the corresponding actual values. This allows us to appraise, for each aggregate, whether the simulated values track the observed data.Macroeconometric models; Economic Policy

In vivo evidence of epiretinal membrane formation secondary to acute macular microhole after posterior vitreous detachment

The authors present the case of an idiopathic epiretinal membrane (ERM) secondary to an acute self-repaired macular microhole documented by optical coherence tomography (OCT). A 65-year-old phakic woman presenting with acute onset of light flashes, myodesopsia, and central negative scotoma in the right eye was diagnosed with age-related posterior vitreous detachment. Spectral-domain OCT showed a tiny defect of the outer retina, consistent with the diagnosis of macular microhole, which spontaneously closed after 4 weeks. Six months later, the patient developed a contracting ERM, and her visual acuity significantly worsened. This case represents the first in vivo documentation of retinal pigment epithelium cell migration through a macular microhole, highlighting the importance of OCT in understanding idiopathic ERM pathogenesis

The Italian Treasury Econometric Model (ITEM)

In this paper, we provide a description of the Italian Treasury Econometric Model (ITEM). We illustrate its general structure and model properties, especially with regard to the economy's response to changes in policy and in other dimensions of the economic environment. One of the key features of the model is the joint representation of the economy on both the demand and the supply sides. Since it is designed for the needs of a Treasury Department, its public finance section is developed in great detail, both on the expenditure and revenue sides. It also features a complete modeling of financial assets and liabilities of each institutional sector. After documenting the model structure and the estimation results, we turn to the outcomes of model simulation and ascertain the model properties. In ITEM the shocks that generate permanent effects on output are associated with: a) variables that affect the tax wedge in the labor market and the user cost of capital; b) labor supply change; and c) variation in the trend component of TFP (technical progress). By contrast, demand shocks have only temporary effects on output (c) 2009 Elsevier B.V. All rights reserved

The Italian Treasury Econometric Model (ITEM)

In this paper, we provide a description of the Italian Treasury Econometric Model (ITEM). We illustrate its general structure and model properties, especially with regard to the economy's response to changes in policy and in other dimensions of the economic environment. One of the key features of the model is the joint representation of the economy on both the demand and the supply sides. Since it is designed for the needs of a Treasury Department, its public finance section is developed in great detail, both on the expenditure and revenue sides. It also features a complete modeling of financial assets and liabilities of each institutional sector. After documenting the model structure and the estimation results, we turn to the outcomes of model simulation and ascertain the model properties. In ITEM the shocks that generate permanent effects on output are associated with: a) variables that affect the tax wedge in the labor market and the user cost of capital; b) labor supply change; and c) variation in the trend component of TFP (technical progress). By contrast, demand shocks have only temporary effects on output.Macroeconometric models Economic policy

Cystoid macular edema secondary to paclitaxel therapy for ovarian cancer: A case report

Paclitaxel is a member of the taxane agents that has demonstrated efficacy in ovarian cancer, both in first- and in second-line therapy. Counted among the side-effects of this drug are neurological disorders. In the present study, a rare case of a non-neuropathic ocular disorder, known as cystoid macular edema (CME), due to paclitaxel in patients treated for ovarian cancer is described. Macular edema, or CME, is a common cause of visual impairment that has been classically demonstrated by fluorescein angiograms, showing capillary leakage. CME without fluorescein leakage is rare, and its most common causes are juvenile X-linked retinoschisis, Goldmann-Favre syndrome, and niacin toxicity. At the present time, the mechanism associated with the form of CME that does not exhibit any signs of fluorescein leakage has not been elucidated due to an absence of histopathological studies. Several mechanisms have been proposed, although it is considered to occur due to disruption of the normal blood-retinal barrier by molecules with a molecular weight lower than that of fluorescein, which leads to fluid accumulation in the intracellular space. It is well known that taxane agents cause fluid retention, represented by edema, weight gain, and third-space fluid collection (pericardial, pleural, ascites), and this appears to be associated with their cumulative dose. The present case study confirms that macular edema associated with paclitaxel use exhibits spontaneous resolution following discontinuation of the causative agent. Taxane-associated maculopathy has been scarcely reported in the literature, but the gynecological oncologist should be alert to its possible development, and an ophthalmologic evaluation should be offered to all patients using paclitaxe

HPV-Negative Cervical Cancer: A Narrative Review

Cervical cancer (CC) is the fourth most frequent cancer in women worldwide. HPV infection is associated with the majority of CC cases, but a small proportion of CCs actually test negative for HPV. The prevalence of HPV among CC histotypes is very different. It has been suggested that HPV-negative CC may represent a biologically distinct subset of tumors, relying on a distinct pathogenetic pathway and carrying a poorer prognosis, than HPV-positive CCs. Although, the discordance in terms of sensitivity and specificity between different HPV tests as well as the potential errors in sampling and storing tissues may be considered as causes of false-negative results. The identification of HPV-negative CCs is essential for their correct management. The aim of this narrative review is to summarize the clinical and pathological features of this variant. We also discuss the pitfalls of different HPV tests possibly leading to classification errors

A machine learning approach applied to gynecological ultrasound to predict progression-free survival in ovarian cancer patients

In a growing number of social and clinical scenarios, machine learning (ML) is emerging as a promising tool for implementing complex multi-parametric decision-making algorithms. Regarding ovarian cancer (OC), despite the standardization of features that can support the discrimination of ovarian masses into benign and malignant, there is a lack of accurate predictive modeling based on ultrasound (US) examination for progression-free survival (PFS). This retrospective observational study analyzed patients with epithelial ovarian cancer (EOC) who were followed in a tertiary center from 2018 to 2019. Demographic features, clinical characteristics, information about the surgery and post-surgery histopathology were collected. Additionally, we recorded data about US examinations according to the International Ovarian Tumor Analysis (IOTA) classification. Our study aimed to realize a tool to predict 12 month PFS in patients with OC based on a ML algorithm applied to gynecological ultrasound assessment. Proper feature selection was used to determine an attribute core set. Three different machine learning algorithms, namely Logistic Regression (LR), Random Forest (RFF), and K-nearest neighbors (KNN), were then trained and validated with five-fold cross-validation to predict 12 month PFS. Our analysis included n. 64 patients and 12 month PFS was achieved by 46/64 patients (71.9%). The attribute core set used to train machine learning algorithms included age, menopause, CA-125 value, histotype, FIGO stage and US characteristics, such as major lesion diameter, side, echogenicity, color score, major solid component diameter, presence of carcinosis. RFF showed the best performance (accuracy 93.7%, precision 90%, recall 90%, area under receiver operating characteristic curve (AUROC) 0.92). We developed an accurate ML model to predict 12 month PFS

A Machine Learning Tool to Predict the Response to Neoadjuvant Chemotherapy in Patients with Locally Advanced Cervical Cancer

Despite several studies having identified factors associated with successful treatment outcomes in locally advanced cervical cancer, there is the lack of accurate predictive modeling for progression-free survival (PFS) in patients who undergo radical hysterectomy after neoadjuvant chemotherapy (NACT). Here we investigated whether machine learning (ML) may have the potential to provide a tool to predict neoadjuvant treatment response as PFS. In this retrospective observational study, we analyzed patients with locally advanced cervical cancer (FIGO stages IB2, IB3, IIA1, IIA2, IIB, and IIIC1) who were followed in a tertiary center from 2010 to 2018. Demographic and clinical characteristics were collected at either treatment baseline or at 24-month follow-up. Furthermore, we recorded data about magnetic resonance imaging (MRI) examinations and post-surgery histopathology. Proper feature selection was used to determine an attribute core set. Three different machine learning algorithms, namely Logistic Regression (LR), Random Forest (RFF), and K-nearest neighbors (KNN), were then trained and validated with 10-fold cross-validation to predict 24-month PFS. Our analysis included n. 92 patients. The attribute core set used to train machine learning algorithms included the presence/absence of fornix infiltration at pre-treatment MRI as well as of either parametrium invasion and lymph nodes involvement at post-surgery histopathology. RFF showed the best performance (accuracy 82.4%, precision 83.4%, recall 96.2%, area under receiver operating characteristic curve (AUROC) 0.82). We developed an accurate ML model to predict 24-month PFS

A Machine Learning Tool to Predict the Response to Neoadjuvant Chemotherapy in Patients with Locally Advanced Cervical Cancer

Despite several studies having identified factors associated with successful treatment outcomes in locally advanced cervical cancer, there is the lack of accurate predictive modeling for progression-free survival (PFS) in patients who undergo radical hysterectomy after neoadjuvant chemotherapy (NACT). Here we investigated whether machine learning (ML) may have the potential to provide a tool to predict neoadjuvant treatment response as PFS. In this retrospective observational study, we analyzed patients with locally advanced cervical cancer (FIGO stages IB2, IB3, IIA1, IIA2, IIB, and IIIC1) who were followed in a tertiary center from 2010 to 2018. Demographic and clinical characteristics were collected at either treatment baseline or at 24-month follow-up. Furthermore, we recorded data about magnetic resonance imaging (MRI) examinations and post-surgery histopathology. Proper feature selection was used to determine an attribute core set. Three different machine learning algorithms, namely Logistic Regression (LR), Random Forest (RFF), and K-nearest neighbors (KNN), were then trained and validated with 10-fold cross-validation to predict 24-month PFS. Our analysis included n. 92 patients. The attribute core set used to train machine learning algorithms included the presence/absence of fornix infiltration at pre-treatment MRI as well as of either parametrium invasion and lymph nodes involvement at post-surgery histopathology. RFF showed the best performance (accuracy 82.4%, precision 83.4%, recall 96.2%, area under receiver operating characteristic curve (AUROC) 0.82). We developed an accurate ML model to predict 24-month PFS