dataesr/scientific-paragraphs-categorization huggingface dataset:
Methods Hospital discharge databases comparing hospital stays for ischemic stroke associated with dysphagia vs stroke without dysphagia in France and Switzerland were analyzed. The French Medical Information System Program (PMSI) database analysis focused on 62'297 stays for stroke in the public sector. 6'037 hospital stays for stroke were analyzed from the Swiss OFS (Office federal de la statistique: Statistique des couts par cas 2012) database. Diagnosis codes and listing of procedures were used to identify dysphagia in stroke patients.
Results Patients with post-stroke dysphagia accounted for 8. 4% of stroke hospital stays in Switzerland, which is consistent with recently reported prevalence of dysphagia at hospital discharge  (Arnold et al, 2016). The French database analysis identified 4. 2% stays with poststroke dysphagia. We hypothesize that the difference between the Swiss and French datasets may be explained by the limitations of an analysis based on diagnosis and procedure coding. Patients with post-stroke dysphagia stayed longer at hospitals (LOS of 23. 7 vs. 11. 8 days in France and LOS of 14. 9 vs. 8. 9 days in Switzerland) compared with patients without post-stroke dysphagia. Post-stroke dysphagia was associated with about €3'000 and CHF14'000 cost increase in France and Switzerland respectively.
Database Hospital discharge databases comparing hospital stays for stroke associated with dysphagia vs stroke without dysphagia in France and Switzerland database were analyzed: (demande_base@atih. sante. fr), on condition that the analyses envisaged have been authorised by the French Data Protection Agency (Commission nationale de l'Informatique et des Libertes, 3 Place de Fontenoy, 75,007 Paris, France; https://www. cnil. fr/fr/declarer-un-fichier). Access to the OFS database is made available to researchers on reasonable demand, upon request to the relevant agency, the OFS, Espace de l'Europe 10, CH-2010 Neuchatel, Switzerland (www. statistik. admin. ch). Funding: This study was sponsored by Nestle Health Science, Nestec S. A. , Vevey, Switzerland, through research contracts with HEVA, Lyon, France to L.
Selection of study cohort CIM10 (French adaptation of ICD10) and ICD10-GM (International Classification of Diseases-10th version-German Modification) diagnosis codes as well as CCAM (Classification Commune des Actes Medicaux, the French listing of all medical and surgical procedures) and CHOP (the Swiss listing of medical/surgical/interventional procedures) were used to identify stroke patients stays with and without dysphagia. Relevant diagnosis and procedure codes were selected with coding physicians and stroke neurologists. Hospital stays were selected from the 2012 dataset by combining several ICD10 codes and excluding some diagnoses to identify stroke patients: -Stays with codes for Cerebral infarction and Sequelae of cerebral infarction (I63 and I69. 3) as main diagnoses were included (DP-Diagnostic Principal in France and HD in Switzerland) Because the French public sector accounted for 97% of all hospitalization for stroke, further analysis of French data focused on the public sector only.
62'297 stays for stroke in the French public sector and 11'091 hospital stays for stroke were identified in France and Switzerland respectively (MS database). Further analysis for Switzerland focused on 6'037 hospital stays for stroke available in the FK database. To identify hospital stays with dysphagia, we analyzed codes of dysphagia as secondary diagnosis and procedures codes (CCAM in France and CHOP in Switzerland) related to dysphagia, such as PEG (Percutaneous Endoscopic Gastrostomy) or other types of gastrostomy, feeding tube insertion, FEES (Fiberoptic Endoscopic Evaluation of Swallowing) or VFSS (Videofluoroscopic Swallow Study), evaluation and re-education of swallowing using instrumental control and rehabilitation, adaption, compensation or therapy of swallowing.
Analysis of burden of post-stroke dysphagia A descriptive analysis of hospital stays for each group (stroke stay with and without dysphagia) was performed based on number of hospital stays and age and sex of patients. In order to eliminate confounding factors for the comparison, data from both study groups from databases were matched according to age, sex and stroke complications (no complications, motor complication, sensory complications, motor and sensory complications). Stroke stays with motor or sensory complications were identified by secondary diagnosis codes (ICD10 codes). Comparison of LOS and costs were then performed using the Mann-Whitney test, with and without matching, in order to assess the burden of post-stroke dysphagia. The hospital perspective was used to assess costs (costing year 2013).
Results Patients with post-stroke dysphagia accounted for 8. 4% of stroke hospital stays in Switzerland. French database analysis identified 4. 2% stays with post-stroke dysphagia (Tables  1  and 2 ). Post-stroke dysphagic patients stayed longer at hospitals (LOS of 23. 7 vs. 11. 8 days in France and LOS of 14. 9 vs. 8. 9 days in Switzerland) as compared to post-stroke patients without dysphagia. Post-stroke dysphagia was associated with 2'926 and CHF13'959 cost increase in France and Switzerland respectively (Tables  3  and 4 ). Additional analysis was performed to test for motor and sensory complications of stroke as confounding factors for hospital LOS and costs. The Mann-Whitney test was used to evaluate the impact of dysphagia on hospital LOS and costs for each of four subgroups: stays with no complications, motor complication, sensory complications and stay with both motor and sensory complications.
Discussion The reported frequency of post-stroke dysphagia varies considerably depending on the definition and assessment method used as well as the time of dysphagia evaluation. In this study, patients with dysphagia accounted for 8. 4% of post-stroke hospital stays in Switzerland, which is consistent with recently reported 10% frequency of dysphagia at hospital discharge  [4]. A recent study on cost of post-stoke dysphagia in Medicare patients also found 9. 9% stroke patients with diagnosis of dysphagia according to ICD-9 coded assigned upon discharge from hospital  [5]. The French database analysis identified 4. 2% stays with post-stroke dysphagia. The French study on prevalence of stroke and its sequelae reported dysphagia prevalence of 13. 3%  [6]. We hypothesize that the difference between the Swiss and French datasets analyzed and overall lower prevalence of dysphagia in this study may be explained by the limitations of the methodological approach (analysis based on diagnosis and procedure coding).
A limitation of our study methodology using the hospital discharge databases was unavailability of data on stroke severity in the database, as potential confounder. Another limitation of our study was reliance on procedure coding to identify patients with dysphagia which can lead to underestimation of the true frequency of the problem. Because bedside clinical examination is usually most frequently used method to assess dysphagia in clinical practice, not all patients with dysphagia are referred to FEES or VFSS. On the other hand, not all patients referred for FEES or VFSS would have confirmed diagnosis of dysphagia by these instrumental methods. On the other hand, conducting analysis on national level reflects real world clinical practice of dysphagia diagnosis and management as compared to controlled study on selected centers.
French Medical Information System Program (Programme de Medicalisation des Systèmes d'Information-PMSI) 2012 database -Swiss OFS (Office federal de la statistique: Statistique des couts par cas) 2012 database The French PMSI-MCO (medecine, chirurgie, obstetrique) is an exhaustive medico-administrative hospital discharge database which covers all overnight or day hospitalizations in the public and private sectors involving short-term stays in medical, surgical or obstetric facilities. It collects information on the patients and their diagnoses (primary diagnoses (DP-diagnostic principal /DR-diagnostic relie) and comorbidities or complications (DAS-Diagnostic associesignificatif)) in CIM10 (Classification internationale des maladies 10ème version) and surgical/interventional procedures (CCAM-Classification Commune des Actes Medicaux).
Detailed production costs were issued from ENCC (Echelle Nationale des Couts à methodologie Commune), a sample of more than 150 private and public hospitals. The MS (Statistique medicale des hopitaux) database collects all patients' stays in Switzerland. The information gathers patient's characteristics, diagnoses (ICD10-GM -Internationale statistische Klassifikation der Krankheiten und verwandter Gesundheitsprobleme, 10. Revision, German ModificationDie "Internationale statistische Klassifikation der Krankheiten und verwandter Gesundheitsprobleme" (ICD-10) wird von der) which can be HD (Hauptdiagnosen, main diagnosis-the condition which requested the highest amount of resources during the hospital stay) or ND (Nebendiagnosen, secondary diagnosis-comorbidities or complications), medical/surgical/interventional procedures (CHOP-Schweizerische Operationsklassifikation), as well as detailed production costs documented in addition.
- French Medical Information System Program (Programme de Medicalisation des Systèmes d'Information-PMSI) 2012 database -Swiss OFS (Office federal de la statistique: Statistique des couts par cas) 2012 database The French PMSI-MCO (medecine, chirurgie, obstetrique) is an exhaustive medico-administrative hospital discharge database which covers all overnight or day hospitalizations in the public and private sectors involving short-term stays in medical, surgical or obstetric facilities. It collects information on the patients and their diagnoses (primary diagnoses (DP-diagnostic principal /DR-diagnostic relie) and comorbidities or complications (DAS-Diagnostic associesignificatif)) in CIM10 (Classification internationale des maladies 10ème version) and surgical/interventional procedures (CCAM-Classification Commune des Actes Medicaux).
Barrett's oesophagus is a condition characterised by metaplastic changes in the oesophagus and is a precursor to oesophageal adenocarcinoma. Oesophageal adenocarcinoma is recognised as being responsible for an increasing number of cancerrelated deaths, especially in the western world. A deluge of risk factors have been described in the literature. Some of the important ones include gastroesophageal reflux disease, Helicobacter pylori infection, lifestyle factors like alcohol consumption, smoking, and dietary factors, and metabolic diseases like obesity. It also poses challenges in diagnosis and treatment despite recent advances in diagnostics, surgery, and other therapies. This is a narrative review of the findings of multiple studies that were retrieved from electronic databases like PubMed, Google Scholar, Scopus, Medline, Embase, and Cochrane.
Obesity and metabolic syndrome The incidence of BO and OAC has increased rapidly in the past 40-50 years in western countries, parallel with rapid increases in the rate of obesity  [68, 69]. Obesity has been linked to a higher risk for OAC and to BO, a predisposing lesion for OAC  [70, 71]. For every 1 kg/m 2 increase in body mass index (BMI) OAC risk increased by 16% and BO risk increased by 12%  [71]. A pooled analysis from the International BEACON Consortium showed that the association of OAC and oesophagogastric junction adenocarcinomas increased directly with increasing BMI. Compared to a BMI of < 25 kg/m 2 a BMI of 30. 0-34. 9 kg/m 2 was associated with a two-fold increased risk of OAC and 4-to 5-fold risk with BMI ≥ 40. 0 kg/m 2  [72, 73]. Analysis of the SEER database in the United States found OAC to be significantly associated with metabolic syndrome (OR = 1.
Facts According to Molloy, Smith, and Wozniak (2011), who use three publicly available datasets (American Community Survey (ACS), the Annual Social and Economic Supplement to the CPS (March CPS), and Internal Revenue Service (IRS) data), each year roughly 5% of the population moves between counties each year, which amounts to roughly one-third of the annual flows into and out of employment according to the measure in Fallick and Fleischman (2004). The cross-state figure is 2%, and the cross-census division rate is estimated at 1 5% of the population, per year (see Table  1 ). It is somewhat unfortunate that none of the datasets employed by Molloy, Smith, and Wozniak (2011) are well suited for the purpose of analyzing migration and ownership. None of them track movers, so it is impossible to know the circumstances of an individual at the moment they decided to move, which is ultimately of interest in this Quantitative Economics 10 (2019) paper.
4 I therefore use the Survey of Income and Program Participation (SIPP) in this paper, a longitudinal and nationally representative dataset. 5 efore presenting statistics from SIPP data, I will explain the geographic concept I will be using in this paper, which is a US census division. Census divisions are nine relatively large regions which separate the United States into groups of states "for the presentation of census data. " 6 To a first approximation, those regions represent areas with a common housing and labor market. In the model, a move within any region is not considered as migration and, therefore, does not contribute to the overall migration rate. This implies that there is a proportion of moves across markets that do happen in the data, but which are not picked up by my geographic definition of a market.
Homeownership and college education are important predictors for migration Putting somewhat more structure onto this, I next present estimates from a statistical analysis of the determinants of cross-division moves from household-level SIPP data. I combine four panels of SIPP data (1996, 2001, 2004, and 2008) into a database with 102,529 household heads that I can follow over time and space. This will be the central estimation sample in main analysis below. Table  3  shows the results. 9 I regress a binary indicator for whether or not a cross-division move took place in a given year on a set of explanatory variables, which relate to the household in question in a probit regression. The table shows marginal effects computed at the sample mean of each variable, Quantitative Economics 10 (2019) as well as the ratio of marginal effects to the baseline unconditional probability of moving (1 32%).
Estimation of exogenous processes VAR process for aggregates Q t and P t The VAR processes at the aggregate and regional level are estimated using a seemingly unrelated regression with two equations, one for each factor Q t and P t t = 1967 2012. I use real GDP per capita as a measure for Q t , and the Federal Housing and Finance Association (FHFA) US house price index for P t. Given that I am interested in the level of house prices (i. e. , a measure of house value), I compute the average level of house prices found in SIPP data for the year 2012 and then apply the FHFA index backwards to construct the house value for each year. 22  reproduce equation (2) here for ease of reading: F t = AF t-1 + ν t-1 ν t ∼ N 0 0 Σ 22 The GDP series is as provided by the Bureau of Economic Analysis through the FRED database.
Quantitative Economics 10 (2019) The effect of homeownership on the option value 1473 F t = Q t P t The estimates from this equation are given in Table  5  and are used by agents in the model to predict F t+1 given F t. Aggregate to regional price mappings The series for q dt is constructed as per capita personal income by region, with a measure of personal income obtained from the Bureau of Economic Analysis and population counts by state from intercensal estimates from the Census Bureau. The price series by region, p dt , comes from the same FHFA dataset as used above: q dt p dt = a d F t + η dt η dt ∼ N 0 0 Ω d (20) The performance of this model in terms of delivered predictions from the aggregate state can be gauged visually in Figures  3  and B. 3 in the Online Appendix B, which also contains the respective parameters estimates in Table  B.
The prediction is obtained from the VAR model in (3), which relates the aggregate series {Q t P t } 2012 t=1968 to mean labor productivity {q dt } 2012 t=1968 for each region d. Agents use this prediction in the model, that is, from observing an aggregate value F t = (P t Q t ) they infer a value for q dt for each region above. as this of course depends on the value of the estimated parameters a d. An illustration of this translation of shocks is shown in Online Appendix B. 4. Finally, a reasonable concern is how good an approximation of a more fine-grained geography such as state-level this setup based on divisions is. In order to shed some light on this, I run pooled OLS on my entire prices dataset, where on the left-hand side I have the price index for state s in period t, p st , and as explanatory variable the corresponding census division level index, p dt.
The derivative is approximated via finite differences, and Σ is obtained by obtaining 400 draws from the moment function. 28 uantitative Economics 10 (2019) Estimation sample My estimation sample is formed mainly out of averages over SIPP data moments covering the period 1997-2012, conditional on noncollege as described above. All moments are constructed using SIPP cross-sectional survey weights, and all dollar values have been inflated to base year 2012 using the BLS CPI for all urban consumers. 29 Averaging over years was necessary to preserve a reasonable sample size in all conditioning cells. However, it also introduces an initial conditions and cohort effects problem, since, for example, a 30-year old in 1997 faced a different economic environment over their lifecycle than a similar 30-year old in 2012 would have.
The challenge is to construct an artificial dataset from simulated data, which has the same time and age structure as the sample taken from the data-in particular, agents in the model should have faced the same sequence of aggregate shocks as their data counterparts from the estimation sample. This requires to simulate individuals starting in different calendar years, taking into account the actual observed time series for regional house prices and incomes. Identification Identification is achieved by comparing household behaviour under different price regimes. The variation comes from using the observed house price and labor productivity series in estimation, which vary over time and by region. The identifying assumption is that, conditional on all other model features, households must be statistically identical across those differing price regimes.
Conclusion The main result of this paper is to show that despite average migration rates being low, the option value associated with the possibility to leave a location in a world with regional shocks to house prices and labor income is large. Removing the option to migrate in the model implies an associated reduction in per period consumption that ranges from 0% (or even implied increases in consumption) up to 19% depending on the type of household one considers. Variations in this measure vary widely with age, ownership status, original location, and point in the business cycle. To arrive at this result, I construct a lifecycle model which includes homeownership as a choice variable next to savings and location choices, which I then fit to SIPP data and use to make counterfactual experiments.
Introduction In 1915, José María Dusmet y Alonso described the bee Anthidium ordonezi from the town of Mogador, today's Essaouira, on the Atlantic coast of Morocco  (Dusmet, 1915). Pasteels (1969)  classified this taxon as a subspecies of Rhodanthidium sticticum  (Fabricius, 1787)  ("sticticum F. (ordonezi Dusmet)"). In his overview of the bees of the tribe Anthidiini of the Western Palearctic,  Warncke (1980)  accepted this assignment, but did not recognize Pasteel's generic classification. Subsequently, ordonezi was accepted as a subspecies of sticticum, either as Anthidium sticticum ordonezi or as Rhodanthidium sticticum ordonezi (e. g. ,  Ornosa et al. , 2008; Kuhlmann, 2016;  http://westpalbees. myspecies. info), including in online databases such as Encyclopedia of Life (http://eol. org), BioLib.
Material Materials deposited in the Senckenberg Museum, Frankfurt am Main (Germany) (SMF), the Biologiezentrum -Oberösterreichisches Landesmuseum, Linz (Austria) (OLL), and the private collection of Maximilian Schwarz (Ansfelden, Austria) (cMS) were examined. The search for faunistic literature records was concentrated on Morocco and Algeria. The faunistic references for R. sticticum for other areas of the Mediterranean are not necessarily complete. Data from the Snow Entomological Collection (University of Kansas, Lawrence, KS, USA) (SEMC) were downloaded from the SEMC database at https:// biodiversity. ku. edu/node/1095. Data from the Global Biodiversity Information Facility (GBIF) database (www. gbif. org) were also checked but did not provide additional information, particularly as human observations were not used here due to possible confusion with R.
Methods In order to discover morphological differences between the two taxa, 11 morphological traits were measured (Table ; see also  Kasparek, 2018). Body parts were photographed for this purpose under a stereozoom microscope (constant 2× magnification) and subsequently measured with TSView7 (v. 7. 3. 1. 7) software. To obtain exact measurements, it is critical that the points of reference be equidistant from the lens of the microscope. This is challenging because of the three-dimensional shape of the specimens and requires some experience on the part of the observer. All measurements were taken by the same person and with the same instruments. The morphometric data were analyzed with discriminant function analysis (DFA) (=canonical variate analysis, CVA) to determine whether a set of body measurements was effective in predicting category membership.
Data and statistical analysis Length of each allele was scored and binned in GENEIOUS R10 (https://www. geneious. com). FSTAT was used to estimate allelic richness (Ar) based on minimal population size from the smallest group in the study  (18 individuals). Ar describes genetic variation while eliminating the effect of the sample size. Estimates of expected heterozygosity (H E ), observed heterozygosity (H O ), inbreeding coefficient (F IS ) and number of private alleles for each population were calculated in software GENEALEX 6. 501. Pairwise fixation index values (F ST ) and Hardy-Weinberg (H-W) test for heterozygote deficiency were calculated in GENEPOP software  [23, 24]. Exact p-values for H-W test were calculated using a Markov chain algorithm  [25]  with 1000 dememorization steps for 500 batches and 1000 iterations per batch.
Software POPULA-TIONS  [26]  was used to compute a matrix of minimum genetic distances according to Nei  [27]  for all individuals. This matrix was used to construct a phylogenetic neighbour-joining tree of relationships among populations and individuals. The tree was graphically visualized in FIGTREE  [28]. Visualization of genetic relationships between individuals was processed in GENETIX software  [29]  using factorial correspondence analysis (FCA). To assign particular genotypes to respective clusters (K) and to assess substructure within the dataset, Bayesian clustering approach implemented in software STRUCTURE  [30]  was used. Number of tested K ranged from 1 to 10. For each value of K, five runs were performed with a burn-in period of 300 000 and 1 000 000 MCMC (Markov chain Monte Carlo) repetitions.