dataesr/scientific-paragraphs-categorization huggingface dataset:
As the data for costs were skewed, boot strap estimation was used to derive 95% confidence intervals of mean cost differences between the groups  [45]. Costs for mothers and children, respectively, were compared before (longitudinal control group) and after (intervention group) the introduction of Familystart and displayed as boxplots up to the sixth month after delivery. For this analysis, an individual perspective was chosen, starting with the day of the woman's hospital entry. The in-hospital period was assessed as t 0 and 30-day periods after hospital discharge were labelled as month1, month2, etc. up to the end of the treatment period. In case of overlap of treatment between these monthly periods, costs were assigned proportionally to the duration of the treatment. Of the 236 women having delivered in BS, the exact date of delivery was unavailable for 56, thus they were excluded from the post-delivery cost calculation.
Subsequently, monthly costs of the intervention canton (intervention group) were compared to those in the five control cantons (cross-sectional control group). For this analysis, costs were compared per calendar month, from January 2012 to May 2013. Furthermore, the duration of pre-and post-delivery hospital stay was calculated and analysed analogously. DRG codes were grouped into four categories: vaginal delivery without (V) and with complications (Vk) and caesarean section without (C) and with complications (Ck). Additional information was extracted from the Helsana Group database to control for possible confounding factors: mother's age, insurance type (private/ semi-private, basic), amount of the deductible (a flexible amount of out-of-pocket expenses insured persons can chose. The higher the deductible, the lower are insurance fees), length of hospital stay, and use of midwifery service.
The study has several limitations and strengths worth mentioning. The intervention group was small, limiting the power of detecting differences, for instance, for hospital re-admissions. Also, we cannot exclude some residual confounding by information not assessed in the database, such as parity or age of children at discharge. If mothers insured by the Helsana Group differ from mothers insured elsewhere with regard to risks in pregnancy and delivery, this might affect the findings. We have, however, no evidence that there is a positive or negative risk selection associated with insurance from the Helsana Group. A strength of the study is that we could take into account costs after hospital discharge for mothers and infants over a period of six months and costs could be retrieved for 97% of all claims relating to the study period.
Material and methods subjects This monocentric retrospective study included 50 patients with mild-to-severe COPD, managed by outpatient PR. The severity of COPD was evaluated by the Global Initiative for Chronic Obstructive Lung Disease stage (Stage I: mild, FEV 1 / FVC ,0. 70 and FEV 1 $80% predicted; Stage II: moderate, FEV 1 /FVC ,0. 70 and 50% # FEV 1 ,80% predicted; Stage III: severe, FEV 1 /FVC ,0. 70 and 30% # FEV 1 ,50% predicted; Stage IV: very severe, FEV 1 /FVC ,0. 70 and FEV 1 ,30% predicted or FEV 1 ,50% predicted plus chronic respiratory failure). 24 All patients included in this study had pre-rehabilitation 6MWT, 6MST, and CPET assessment data available. The study protocol was approved by the Observational Research Protocol Evaluation Committee of the French Language Society of Pulmonology, France (CEPRO n° 2011-036).
Statistical analyses Means ± standard deviations (SD) were used to describe variables. Before using parametric tests, the assumption of normality was verified using the Kolmogorov-Smirnov test. The data were then analysed using multivariate analysis of variance (4x2) with repeated measures on the second factor. The factors included four separate groups of training (PLYO, AG, RS, and CON) and repeated measures of time (pre-and post-training). Because of the slight differences in the initial groups, analysis of covariance with the pre-test values as the covariate was used to determine significant differences between the post-test adjusted means in the groups. An analysis of covariance with the pre-test values as the covariate was used to determine significant differences between the post-test adjusted means in groups.
We demonstrated that increasing array resolution to single exons not only allowed detection of small CNVs in the known disease genes, but also provided new opportunities for novel gene discoveries and the ability to detect somatic mosaicism for intragenic CNVs  [29, 34]. Despite the advances in molecular diagnostics using genome-wide assays  [1] , WES and targeted nextgeneration sequencing (NGS) studies have limited ability to detect small intragenic CNVs  [25]  and therefore cannot currently reveal the totality of disease genes and pathogenic alleles. To further investigate this hypothesis and to assess the efficacy of exon-targeted CMA to identify putative novel disease genes, we queried our database of exon-targeted clinical CMA performed in 63,127 patients at Baylor Genetics (BG) Laboratories, with a particular focus on small de novo autosomal and Xlinked (in males) CNVs involving genes that are currently not associated with human disease.
We used an in-house developed software to detect CNVs from aCGH data. The algorithm requires at least three consecutive probes with a log2 ratio < -0. 6 to detect deletion or at least three consecutive probes with a log2 ratio > 0. 4 to detect duplication. CNVs < 500 kb with no RefSeq genes within the intervals or CNVs within segmental duplication (SDs) and CNVs in benign polymorphic regions, such as those listed in Database of Genomic Variants (DGV) are generally not reported. CNVs located in the regions known to be diseaseassociated and likely exhibiting incomplete penetrance, variable expressivity, or representing a potential recessive carrier state can be reported despite their overlap with DGV CNVs. The remaining CNVs are then classified as: (1) pathogenic = pathogenic CNVs includes aneuploidy, known microdeletion/microduplication, deletion, and intragenic duplication genomic intervals involving known dosage-sensitive autosomal-dominant (AD) disease genes, or deletion of any size involving a dosage sensitive gene associated with AD, haploinsufficiency, or other likely pathogenic consequence.
Moreover large duplications (> 2 Mb) with genes or duplications > 1 Mb with genes known to be dosage-sensitive are also classified as pathogenic CNVs; (2) likely benign = < 1 Mb CNVs with no genes in the intervals or CNVs in the DGV database and have been reported to be inherited multiple times in our CMA database; (3) loss/gain in nondisease region = rare CNVs < 1 Mb with genes not implicated in disease phenotypes; (4) loss/gain of uncertain clinical significance = CNVs that have genes, but there is no supporting evidence of pathogenicity. CNVs of uncertain significance are usually investigated by parental studies. CNVs that have been shown to exhibit incomplete or uncertain penetrance were classified as such. Whole-exome sequencing data WES was performed in ~9000 individuals sequenced at BG and in the cohort of ~6000 samples sequenced in the Human Genome Sequencing Center (HGSC) at BCM through the BHCMG research initiative  [41]  as described previously  [23, [42] [43] [44].
Computational parsing of clinical CMA database The main aim of our retrospective analyses was to identify potential pathogenic or likely pathogenic CNVs affecting genes that thus far were not disease-associated. Subsequently, we queried the BG CMA database for other overlapping submicroscopic CNV deletions up to 5 Mb in size (de novo, inherited, or of unknown parental origin) that encompassed at least one gene that we consider as novel or recently published candidate disease gene. Genes affected by single-gene CNVs and those included in de novo or hemizygous CNVs affecting 2-5 genes were classified according to their disease status using the lists of known AD, autosomal-recessive (AR), or X-linked (XL) disease genes, as defined in the Online Mendelian Inheritance in Man (OMIM) database (http://omim.
org/) [29, 45, 46]. For each gene, we performed an extensive literature search for genotypephenotype correlations. Moreover, all genes were crossreferenced with the Simons Foundation Autism Research Initiative (SFARI) database (https://sfari. org/) of ASD-related genes  [47]. In addition, we searched the BG and BHCMG exome databases for predicted loss-of-function (LOF) (i. e. stopgain, frameshift, or splicing) variants in the candidate genes to potentially provide further evidence of their disease-association. From the initial set, we excluded variants with a total coverage of < 20 reads or the ratio of variant to total reads below 0. 2. We also removed variants with minor allele frequency (MAF) > 0. 001 in ESP  [48] , 1000 Genomes  [49] , or our local exome databases or with MAF > 0. 0001 in ExAC (Exome Aggregation Consortium, http://exac.
2, 3, 4 and 5). However, paternity was not tested formally (i. e. by molecular markers) and thus nonpaternity could not be ruled out for most of the cases. The investigation of ~15,000 WES samples from BG and BHCMG revealed rare LOF variants, providing additional support for potential genotype-phenotype correlations (Tables 1, 2, 3 and 4, patients with variants in ARGLU1, STK3, MEIS2, and PTCHD1, respectively; see also Additional file 3 for discussion on two other candidate disease genes, AGBL4 (OMIM* 616476) and CSMD1 (OMIM* 608397); Additional files 4, 5 and 6 for information on patients with additional variants in ARGLU1/EFNB2, AGBL4, and CSMD1, respectively; and Additional files 7 and 8 for visualization of CNVs in AGBL4 and CSMD1, respectively). ARGLU1 and EFNB2 that map to 13q33 as potential new disease genes In the BG CMA database, we found two CNVs involving ARGLU1 and EFNB2 (OMIM* 600527), including one ~1.
1 Mb de novo deletion encompassing ARGLU1 and EFNB2 and one ~4. 2 Mb deletion of unknown inheritance harboring ARGLU1, DAOA, FAM155A, and EFNB2 (Table  1 ). In addition, one de novo ~3. 8 Mb deletion encompassing ARGLU1 and EFNB2 was found in one DECIPHER (https://decipher. sanger. ac. uk/) patient, 280488. Moreover, in the BG WES database, we identified one de novo frameshift variant (g. 13:107211843delA; NM_018011:c. 509delA; p. K170fs) in ARGLU1. Our further investigation of five novel or very rare missense variants: one in ARGLU1 (c. 350G > A p. R117Q) and four in EFNB2 (c. 498A > C, p. Q166H; c. 503C > T, p. A168V; c. 796A > G, p. T266A; c. 803C > T, p. S268L) revealed that these five variants were all inherited (Additional file 4). Importantly, putative LOF variants in ARGLU1 and EFNB2 have been rarely seen in the BG and BHCMG databases (one variant mentioned above for ARGLU1 and zero for EFNB2),.
STK3 may be associated with human disease phenotypes We identified four different-sized CNV deletions involving STK3 (Table  2 ). Two of these deletions (87 kb and 143 kb in size) were confirmed to be de novo; inheritance of two other CNVs (81 kb and 22 kb in size) could not be determined. In the DECIPHER database, one patient (258095) had an intragenic deletion of exons 5 and 6 of unknown parental origin. Although computationally determined haploinsufficiency and pLI scores for STK3 (0. 12 and 0, respectively) do not favor a haploinsufficiency pathomechanism for this gene, the identification of de novo variants suggest a potential disease association.
Novel MEIS2 and PTCHD1 variants We identified new variants in MEIS2 and PTCHD1, which have been recently recognized as disease-    3 ). Three of them, including one small (~3. 2 kb) and two large (~4. 8 Mb and ~4. 9 Mb) deletions were confirmed to be de novo; one deletion 3. 47 Mb was maternally inherited and the inheritance of two other (~1. 54 Mb and ~0. 6 Mb) CNVs remains unknown. Moreover, an ~909 kb de novo deletion encompassing MEIS2 and three other genes was found in the DECIPHER database (patient 286841). Prediction algorithms indicate MEIS2 as sensitive to LOF with a haploinsufficiency score of 0. 68 and the pLI score of 0. 99. We found three male patients with hemizygous deletions varying in size between 92 kb and 220 kb and encompassing exon 1 (two cases) or exons 2 and 3 of the three-exon PTCHD1 gene on Xp22.
To advance the clinical investigation of CNVs as pathogenic alleles, we studied de novo CNVs and hemizygous deletion CNVs in males, involving both known and candidate disease genes using highresolution exon-targeted clinical CMA data from over 62,000 patients. Our study indicated four genes ARGLU1, STK3, MEIS2, and PTCHD1 as having the strongest evidence for disease association. ARGLU1 (arginine and glutamate rich protein 1) was reported to play a regulatory role in gene transcription through its interaction with MED1 Mediator complexes  [88]. The highest expression level of this gene was found in the cerebellum (GTEx database). The neighboring EFNB2 (Ephrin B2) encodes a member of the Eph receptor family. In previous cytogenetic studies, two large 13q de novo deletions (9 Mb and 28 Mb in size) involving both ARGLU1 and EFNB2 were reported in patients with mild anorectal malformations and EFNB2 was proposed as a good candidate disease gene  [89].
Our patients with a CNV deletion have a relatively milder phenotype, including DD, ASD (three patients), delayed verbal (three patients) and motor milestones (one patient), cleft palate (one patient), and bifid uvula (one patient). In contrast to previously reported cases, none of our patients with CNV deletions had cardiac defects. Similar phenotypic features (asymmetry of the thorax, bifid uvula, and a specific learning disability) were found in one patient with MEIS2 deletion reported in the DECIPHER database. MEIS2 maps to 15q14 distal to the Angelman/Prader-Willi syndromes genomic region on 15q11. 2q12 and the CHRNA7 gene on 15q13. 3 (located 5 Mb from MEIS2). Thus, better understanding of the MEIS2 alternations may help to elucidate the phenotypic spectrum of patients with larger-sized deletions encompassing proximal chromosome 15q.
Identification of additional male patients with CNV deletions or truncating SNVs, involving PTCHD1, further support LOF of this gene as diseasecontributing for non-syndromic neurodevelopmental disorders, including ID, ASD, hypotonia, and behavioral abnormalities  [79, 80]. Importantly, PTCHD1 is listed in the SFARI gene database (https://gene. sfari. org) as a strong candidate gene for ASD and DD/ID. Consistent with this, we identified three male patients with deletions of PTCHD1 who all manifested ASD, DD, or both (Table  4 ). In summary, we applied computational scores that consider the vulnerability of genes to LOF and variant burden. We found that genes with pathogenic variants based on de novo occurrence also tend to have extreme values of haploinsufficiency scores and variant damage or pathogenicity burden.
Workshop Design Patients and clinicians were recruited as experts in decision-making to collect their expert views and insights, stemming from their living with or treating the disease. No clinical data was collected. Both were selected via predefined recruitment criteria (Appendix 1). Clinicians were oncologists specializing in treating patients with locally advanced or metastatic GEP-NET from both academic and community practices. Patients were recruited through patient associations and support groups. All study participants were offered an honorarium as well as reimbursement for their expenses. All participants provided informed consent (patients) or signed agreements (clinicians). To facilitate open discussion, sessions were held under the Chatham House Rule [26] and were limited to six (three patients, three clinicians) and 11 (five patients, six clinicians) participants for the criteria and the decision support workshops, respectively.
Decision Scenarios and Evidence on GEP-NET and Management Options Two decision scenarios were defined on the basis of current practice guidelines  [9] : the first explored the primary decision whether to initiate treatment with an SSA (using lanreotide as reference case) or to pursue watchful waiting; and the second explored the decision between two SSAs treatments: treatment 1 (lanreotide) versus treatment 2 (octreotide). To provide necessary and sufficient evidence to assign a score for each criterion, MCDA evidence matrices for each scenario were created following EVIDEM methodology  [27] , which entails a standardized approach for the identification, analysis, synthesis, and reporting of evidence. Evidence was obtained from public sources, including major biomedical literature databases (PubMed/Medline), Cochrane systematic reviews, clinical trial registries, conference websites (ASCO, NANETS), and bibliographies of full-text articles.
ACKNOWLEDGEMENTS The authors would like to acknowledge the participating physicians and patients. We would like to thank the  Carcinoid Cancer Foundation  for their support in recruiting patients. Funding. This study was sponsored by  Ipsen Pharma. The sponsor also provided funding for the journal's article processing charges and the  Open Access  fee. All authors had full access to all of the data in this study and take complete responsibility for the integrity of the data and accuracy of the data analysis.
We aimed to provide the outlines and the framework of the RENAPE observational registry and share our experience in the establishment of a national patient registry. Results: The RENAPE observational registry has been launched in 2010 thanks to institutional supports. It concerns only patients with a histological diagnosis confirming a peritoneal surface malignancy. A web secured clinical database has been implemented based on data management procedures according to the principles of international recommendations and regulatory statements. A virtual tumor bank is linked in order to the conduct translational studies. Specialized working groups have been established to continuously upgrade and evolve the common clinical and histological data elements following the last classifications and clinical practices.
Diffuse peritoneal leiomyomatosis Diffuse peritoneal leiomyomatosis (DPL) is rare disease. Fewer than 150 cases have been reported in the literature to date. DPL is characterized by the proliferation of multiple benign smooth muscle cell-containing nodules in the peritoneal cavity. DPL manifests during adulthood and is predominantly found in women. Only one case of DPL has been reported in a male  [43]. Malignant transformation is rare and in a few cases  [44]. Etiology is unknown but DPL seems to be a multifactorial disease with a genetic or hormonal component leading to metaplasia of peritoneal mesenchymal cells  [45, 46]. Depending on the extent of the disease, first-line treatment for DPL is surgical excision or CRS  [47]. The establishment of a the French network for rare peritoneal tumors (RENAPE) reference networks with a clinical database linked to a virtual tissue bank aims to improve outcomes and make easier the exchange of experience, information, data and best practices on rare peritoneal malignancies (RPM) amongst all stakeholders  [48].
Objectives and scope By definition, a registry is "an organized system that uses observational study methods to collect uniform data (clinical and others) from individual patient to evaluate specified outcomes for a population defined by a particular disease, condition or exposure, and that serves one or more predetermined scientific, clinical, or policy purposes"  [49]. The RENAPE Registry has been launched in 2010. It aims to monitor more prevalence and incidence of RPM in France, to establish natural history of these rare cancers. It is also intended to assess the clinical effectiveness of new interventions, to measure the quality of care and to provide an inventory of patients to re-contact for participation in epidemiological studies, clinical trials or for health technology assessment to monitor real access to treatments.
Patient population The RENAPE Observational Registry includes only patients with RPM such as peritoneal mesothelioma (ORPHA168811/168816), pseudomyxoma peritonei (ORPHA26790), primary peritoneal serous carcinoma (ORPHA168829), peritoneal desmoplastic small round cell tumors (ORPHA83469), diffuse peritoneal leiomyomatosis (ORPHA71274). All patients were required a confirmation of histological diagnosis as determined by an expert pathologist  [48]. The RENAPE Observational Registry database concerns only persons whose usual place of residence is France.
Data entry and management For each new patient a minimal data set is required (Table  1 ) including identification, demographic information and etiology. The RENAPE Observational Registry employs internal record linkage to identify and eliminate duplicate patient and tumor records. Each entry in the registry has a unique identifier (alphanumeric code). Identification codes are stored in an independent and safe database according to the French Data Protection Act. Patients are written and oral informed that a pseudonymisation method replacing any identifying characteristics of registered personal data with a pseudonym (alphanumeric code) provides a limited protection for the identity of data subjects. However all aggregated data or results provided from the registered data are strictly anonymous.
The clinical data capture will be updated appropriately based on each patient's clinical management. Clinical data are directly collected in a standard format by the clinicians. A resource persona clinical research assistantwith specialized knowledge in the clinical pathways of the RPM -is provided by the coordination of the RENAPE Registry in order to help sites on data entry process. The RENAPE Registry common data elements are organized into 9 categories: patient information, preoperative work-up, peroperative data and 90-day postoperative follow-up (Table  2 ) and long term follow-up. We use preferentially appropriate scores (Peritoneal Cancer Index -PCI, completeness of cytoreduction score -CC-score) and validated classifications (CTCAE). Clear, operational definitions of data elements and standard instructions were provided to collect data consistently.
At each participating site, a local pathologist validates and records histological subtype and grade of tumor on extended data set available to capture pathological details (Table  3 ). Correlation edits check the compatibility of different data elements within a record. These verifications are done as data are loaded into the database and any records failing edits are rejected and returned to the participating sites for verification and/ or correction. The data management procedures follow in accordance with the principles of Good Clinical Practices (GCP). The scope of these standard operating procedures is to: staff training and qualifications, case inclusion, case ascertainment, procedures for adding new cases to the permanent data set, rules for updating or changing data on file, follow-up, data exchange.
Data protection The EOL © application is validated under 21 Code of Federal Regulations criteria (CFR Part 11)  [50]. Data protection is based on the definition of authorization profiles, defining the functions or types of information available to a user. The access to the electronic data capture application is controlled by personal username and password generated by the administrator. All recorded data must be validated by electronic signature. An audit trail module allows tracking of all accesses, modifications, and deletions of data. All exported files are archived with history within the system. The automatic identification of patients without nominative data enables an interoperability of the data recorded and remains pseudonymous. The RENAPE Registry benefits of a safe and secure hosting in France based on Information Technology Infrastructure Library (ITIL) security management (version 3).