Classifying Italian newspaper text: news or editorial?

We present a text classifier that can distinguish Italian news stories from editorials. Inspired by earlier work on English, we built a suitable train/test corpus and implemented a range of features, which can predict the distinction with an accuracy of 89,12%. As demonstrated by the earlier work, such a feature-based approach outperforms simple bag-of-words models when being transferred to new domains. We argue that the technique can also be used to distinguish opinionated from non-opinionated text outside of the realm of newspapers.Presentiamo una tecnica per la classificazione di articoli di giornale in italiano come articoli di cronaca oppure editoriali. Ispirandoci a precedenti pubblicazioni riguardanti la lingua inglese, abbiamo costruito un corpus adatto allo scopo e selezionato un insieme di caratteristiche testuali in grado di distinguere il genere con un accuratezza dell’ 89,12%. Come dimostrato dai lavori precedenti, questo approccio basato sulle proprietà del testo mostra risultati migliori rispetto ad altri quando trasferito a nuovi argomenti. Riteniamo inoltre che questa tecnica possa essere usata con successo anche in contesti diversi dagli articoli di giornale per distinguere testi contenenti opinioni dell’autore e non

The Diverticular Disease Registry (DDR Trial) by the Advanced International Mini-Invasive Surgery Academy Clinical Research Network: Protocol for a Multicenter, Prospective Observational Study

Diverticular disease is an increasingly common issue, with a variety of clinical presentations and treatment options. However, very few prospective cohort studies explore outcomes between the different presentations and treatments. The Diverticular Disease Registry (DDR Trial) is a multicenter, prospective, observational cohort study on behalf of the Advanced International Mini-Invasive Surgery (AIMS) academy clinical research network. The DDR Trial aims to investigate the short-term postoperative and long-term quality of life outcomes in patients undergoing surgery or medical treatments for diverticular disease. DDR Trial is open to participation by all tertiary-care hospitals. DDR Trial has been registered at ClinicalTriats.gou (NCT 04907383). Data collection will be recorded on Research Electronic Data Capture (REDCap) starting on June 1 , 2021 and will end after 5 years of recruitment. All adult patients with imaging-proven colonic diverticular disease (i.e., symptomatic colonic diverticulosis including diverticular bleeding, diverticulitis, and Symptomatic Uncomplicated Diverticular Disease) will be included. The primary outcome of DDR Trial is quality of life assessment at 12-month according to the Gastrointestinal Quality of Life Index (GIQLI). The secondary outcome is 30-day postoperative outcomes according to the Clavien-Dindo classification. DDR Trial will significantly advance in identifying the optimal care for patients with diverticular disease by exploring outcomes of different presentations and treatments

Colorectal surgery in Italy during the Covid19 outbreak: a survey from the iCral study group

Background The COVID19 pandemic had a deep impact on healthcare facilities in Italy, with profound reorganization of surgical activities. The Italian ColoRectal Anastomotic Leakage (iCral) study group collecting 43 Italian surgical centers experienced in colorectal surgery from multiple regions performed a quick survey to make a snapshot of the current situation. Methods A 25-items questionnaire was sent to the 43 principal investigators of the iCral study group, with questions regard- ing qualitative and quantitative aspects of the surgical activity before and after the COVID19 outbreak. Results Two-thirds of the centers were involved in the treatment of COVID19 cases. Intensive care units (ICU) beds were partially or totally reallocated for the treatment of COVID19 cases in 72% of the hospitals. Elective colorectal surgery for malignancy was stopped or delayed in nearly 30% of the centers, with less than 20% of them still scheduling elective colo- rectal resections for frail and comorbid patients needing postoperative ICU care. A significant reduction of the number of colorectal resections during the time span from January to March 2020 was recorded, with significant delay in treatment in more than 50% of the centers. Discussion Our survey confirms that COVID19 outbreak is severely affecting the activity of colorectal surgery centers partici- pating to iCral study group. This could impact the activity of surgical centers for many months after the end of the emergency

ICG fluorescence imaging in colorectal surgery: a snapshot from the ICRAL study group

Background: Fluorescence-guided visualization is a recently proposed technology in colorectal surgery. Possible uses include evaluating perfusion, navigating lymph nodes and searching for hepatic metastases and peritoneal spread. Despite the absence of high-level evidence, this technique has gained considerable popularity among colorectal surgeons due to its significant reliability, safety, ease of use and relatively low cost. However, the actual use of this technique in daily clinical practice has not been reported to date. Methods: This survey was conducted on April 2020 among 44 centers dealing with colorectal diseases and participating in the Italian ColoRectal Anastomotic Leakage (iCral) study group. Surgeons were approximately equally divided based on geographical criteria from multiple Italian regions, with a large proportion based in public (89.1%) and nonacademic (75.7%) centers. They were invited to answer an online survey to snapshot their current behaviors regarding the use of fluorescence-guided visualization in colorectal surgery. Questions regarding technological availability, indications and techniques, personal approaches and feelings were collected in a 23-item questionnaire. Results: Questionnaire replies were received from 37 institutions and partially answered by 8, as this latter group of centers do not implement fluorescence technology (21.6%). Out of the remaining 29 centers (78,4%), fluorescence is utilized in all laparoscopic colorectal resections by 72.4% of surgeons and only for selected cases by the remaining 27.6%, while 62.1% of respondents do not use fluorescence in open surgery (unless the perfusion is macroscopically uncertain with the naked eye, in which case 41.4% of them do). The survey also suggests that there is no agreement on dilution, dosing and timing, as many different practices are adopted based on personal judgment. Only approximately half of the surgeons reported a reduced leak rate with fluorescence perfusion assessment, but 65.5% of them strongly believe that this technique will become a minimum requirement for colorectal surgery in the future. Conclusion: The survey confirms that fluorescence is becoming a widely used technique in colorectal surgery. However, both the indications and methods still vary considerably; furthermore, the surgeons' perceptions of the results are insufficient to consider this technology essential. This survey emphasizes the need for further research to reach recommendations based on solid scientific evidence. Keywords: Colon cancer; Fluorescence guided surgery; ICG; Laparoscopy; Rectal cancer

Bowel preparation for elective colorectal resection: multi-treatment machine learning analysis on 6241 cases from a prospective Italian cohort

background current evidence concerning bowel preparation before elective colorectal surgery is still controversial. this study aimed to compare the incidence of anastomotic leakage (AL), surgical site infections (SSIs), and overall morbidity (any adverse event, OM) after elective colorectal surgery using four different types of bowel preparation. methods a prospective database gathered among 78 Italian surgical centers in two prospective studies, including 6241 patients who underwent elective colorectal resection with anastomosis for malignant or benign disease, was re-analyzed through a multi-treatment machine-learning model considering no bowel preparation (NBP; No. = 3742; 60.0%) as the reference treatment arm, compared to oral antibiotics alone (oA; No. = 406; 6.5%), mechanical bowel preparation alone (MBP; No. = 1486; 23.8%), or in combination with oAB (MoABP; No. = 607; 9.7%). twenty covariates related to biometric data, surgical procedures, perioperative management, and hospital/center data potentially affecting outcomes were included and balanced into the model. the primary endpoints were AL, SSIs, and OM. all the results were reported as odds ratio (OR) with 95% confidence intervals (95% CI). results compared to NBP, MBP showed significantly higher AL risk (OR 1.82; 95% CI 1.23-2.71; p = .003) and OM risk (OR 1.38; 95% CI 1.10-1.72; p = .005), no significant differences for all the endpoints were recorded in the oA group, whereas MoABP showed a significantly reduced SSI risk (OR 0.45; 95% CI 0.25-0.79; p = .008). conclusions MoABP significantly reduced the SSI risk after elective colorectal surgery, therefore representing a valid alternative to NBP

Abdominal drainage after elective colorectal surgery: propensity score-matched retrospective analysis of an Italian cohort

background: In italy, surgeons continue to drain the abdominal cavity in more than 50 per cent of patients after colorectal resection. the aim of this study was to evaluate the impact of abdominal drain placement on early adverse events in patients undergoing elective colorectal surgery. methods: a database was retrospectively analysed through a 1:1 propensity score-matching model including 21 covariates. the primary endpoint was the postoperative duration of stay, and the secondary endpoints were surgical site infections, infectious morbidity rate defined as surgical site infections plus pulmonary infections plus urinary infections, anastomotic leakage, overall morbidity rate, major morbidity rate, reoperation and mortality rates. the results of multiple logistic regression analyses were presented as odds ratios (OR) and 95 per cent c.i. results: a total of 6157 patients were analysed to produce two well-balanced groups of 1802 patients: group (A), no abdominal drain(s) and group (B), abdominal drain(s). group a versus group B showed a significantly lower risk of postoperative duration of stay >6 days (OR 0.60; 95 per cent c.i. 0.51-0.70; P < 0.001). a mean postoperative duration of stay difference of 0.86 days was detected between groups. no difference was recorded between the two groups for all the other endpoints. conclusion: this study confirms that placement of abdominal drain(s) after elective colorectal surgery is associated with a non-clinically significant longer (0.86 days) postoperative duration of stay but has no impact on any other secondary outcomes, confirming that abdominal drains should not be used routinely in colorectal surgery

Blood Transfusions and Adverse Events after Colorectal Surgery: A Propensity-Score-Matched Analysis of a Hen-Egg Issue

Blood transfusions are considered a risk factor for adverse outcomes after colorectal surgery. However, it is still unclear if they are the cause (the hen) or the consequence (the egg) of adverse events. A prospective database of 4529 colorectal resections gathered over a 12-month period in 76 Italian surgical units (the iCral3 study), reporting patient-, disease-, and procedure-related variables, together with 60-day adverse events, was retrospectively analyzed identifying a subgroup of 304 cases (6.7%) that received intra- and/or postoperative blood transfusions (IPBTs). The endpoints considered were overall and major morbidity (OM and MM, respectively), anastomotic leakage (AL), and mortality (M) rates. After the exclusion of 336 patients who underwent neo-adjuvant treatments, 4193 (92.6%) cases were analyzed through a 1:1 propensity score matching model including 22 covariates. Two well-balanced groups of 275 patients each were obtained: group A, presence of IPBT, and group B, absence of IPBT. Group A vs. group B showed a significantly higher risk of overall morbidity (154 (56%) vs. 84 (31%) events; OR 3.07; 95%CI 2.13-4.43; p = 0.001), major morbidity (59 (21%) vs. 13 (4.7%) events; OR 6.06; 95%CI 3.17-11.6; p = 0.001), and anastomotic leakage (31 (11.3%) vs. 8 (2.9%) events; OR 4.72; 95%CI 2.09-10.66; p = 0.0002). No significant difference was recorded between the two groups concerning the risk of mortality. The original subpopulation of 304 patients that received IPBT was further analyzed considering three variables: appropriateness of BT according to liberal transfusion thresholds, BT following any hemorrhagic and/or major adverse event, and major adverse event following BT without any previous hemorrhagic adverse event. Inappropriate BT was administered in more than a quarter of cases, without any significant influence on any endpoint. The majority of BT was administered after a hemorrhagic or a major adverse event, with significantly higher rates of MM and AL. Finally, a major adverse event followed BT in a minority (4.3%) of cases, with significantly higher MM, AL, and M rates. In conclusion, although the majority of IPBT was administered with the consequence of hemorrhage and/or major adverse events (the egg), after adjustment accounting for 22 covariates, IPBT still resulted in a definite source of a higher risk of major morbidity and anastomotic leakage rates after colorectal surgery (the hen), calling urgent attention to the implementation of patient blood management programs

Proceedings of the Fifth Italian Conference on Computational Linguistics CLiC-it 2018

On behalf of the Program Committee, a very warm welcome to the Fifth Italian Conference on Computational Linguistics (CLiC-­‐it 2018). This edition of the conference is held in Torino. The conference is locally organised by the University of Torino and hosted into its prestigious main lecture hall “Cavallerizza Reale”. The CLiC-­‐it conference series is an initiative of the Italian Association for Computational Linguistics (AILC) which, after five years of activity, has clearly established itself as the premier national forum for research and development in the fields of Computational Linguistics and Natural Language Processing, where leading researchers and practitioners from academia and industry meet to share their research results, experiences, and challenges

Efficient Knowledge Compilation Beyond Weighted Model Counting

Quantitative extensions of logic programming often require the solution of so called second level inference tasks, that is, problems that involve a third operation, such as maximization or normalization, on top of addition and multiplication, and thus go beyond the well-known weighted or algebraic model counting setting of probabilistic logic programming under the distribution semantics. We introduce Second Level Algebraic Model Counting (2AMC) as a generic framework for these kinds of problems. As 2AMC is to (algebraic) model counting what forall-exists-SAT is to propositional satisfiability, it is notoriously hard to solve. First level techniques based on Knowledge Compilation (KC) have been adapted for specific 2AMC instances by imposing variable order constraints on the resulting circuit. However, those constraints can severely increase the circuit size and thus decrease the efficiency of such approaches. We show that we can exploit the logical structure of a 2AMC problem to omit parts of these constraints, thus limiting the negative effect. Furthermore, we introduce and implement a strategy to generate a sufficient set of constraints statically, with a priori guarantees for the performance of KC. Our empirical evaluation on several benchmarks and tasks confirms that our theoretical results can translate into more efficient solving in practice

Lifted Reasoning for Combinatorial Counting

Combinatorics math problems are often used as a benchmark to test human cognitive and logical problem-solving skills. These problems are concerned with counting the number of solutions that exist in a specific scenario that is sketched in natural language. Humans are adept at solving such problems as they can identify commonly occurring structures in the questions for which a closed-form formula exists for computing the answer. These formulas exploit the exchangeability of objects and symmetries to avoid a brute-force enumeration of all possible solutions. Unfortunately, current AI approaches are still unable to solve combinatorial problems in this way. This paper aims to fill this gap by developing novel AI techniques for representing and solving such problems. It makes the following five contributions. First, we identify a class of combinatorics math problems which traditional lifted counting techniques fail to model or solve efficiently. Second, we propose a novel declarative language for this class of problems. Third, we propose novel lifted solving algorithms bridging probabilistic inference techniques and constraint programming. Fourth, we implement them in a lifted solver that solves efficiently the class of problems under investigation. Finally, we evaluate our contributions on a real-world combinatorics math problems dataset and synthetic benchmarks.</jats:p