dataesr/scientific-paragraphs-categorization huggingface dataset:
The questionnaire consisted of 34 questions divided into four sections: 1. respondent characteristics, 2. surgical practice, 3. systemic therapy, and 4. health systemrelated factors, and was validated and tested by a clinical working group. The gynecological oncologists/specialist surgeons, medical oncologists, clinical oncologists, and general gynecologists were chosen to receive the questionnaire using existing local networks available to the working group members. Working group members identified distribution lists which included those actively involved in the treatment of ovarian cancer. Country-specific response rates were calculated by dividing the number of responses by the number of questionnaires distributed in each country. Denominators were either database confirmed or estimated by multiplying the number of centers treating ovarian cancer in the country by the approximate number of specialists in each center.
Survey-specific response rates were also calculated using the number of responses from each country. All data were maintained in Microsoft Excel (version 1803). In all tables, figures, and supplementary materials, countries are ordered using the latest 3-year survival figures (2010-2014) from highest (Norway, 57. 2%) to lowest (Ireland, 44. 8%). 3 Comparisons of 3-year survival by 'distant' stage (2010-2014) were performed using Spearman's rho in R Studio (version 3. 5. 1). Information gathered from guidelines and the survey relate to the current time period (~2019) and does not align with the 2010-2014 survival data.
One example of a current national auditing system is the Danish Gynecological Cancer Database, which has collected data on all women with ovarian cancer treated at Danish hospitals since 2005. 38 Another example is the introduction of Scotland's quality performance indicators. 39 National auditing for ovarian cancer has been recommended as a method of investigating treatment disparities and informing quality improvements. 13 Given challenges in comparing ovarian cancer treatment rates internationally, this study supports existing calls for improved data collection at local and national levels. This is particularly pertinent in countries with lower survival, where national audits are not routinely conducted, and highlights a key area of improvement for policy and practice. We note some limitations in this study.
Acknowledgements We thank  Lucie Hooper ,  Shanta Keshwala , and  Charlotte Lynch of Cancer Research UK  for managing the program. We also thank the  International Cancer Benchmarking Partnership Program Board  for their advice; and the  Exploratory Modules Academic Reference  Group for providing independent peer review and advice on the methodological approach and interpretation of findings. Contributors CHN, JBu, RF, andSH: methodology, validation, data collection, analysis, writing -original draft and editing. AA, JBe, SF, MF-K-F, CG, NFH, LH, CKH, GK, JK, OM, GN, AN, DO, TS, and PHS: validation, data collection, analysis, writingreview & editing. CJC: methodolgy, writing -review and editing. PAC: methodology, validation, data collection, analysis, writing -review and editing. EZ: methodology, analysis.
BERENIS Database Base d'Evaluation et de REcherche des Néoplasmes Infiltrants et in Situ (BERENIS) is a database including all patients treated for BC at the Institut de Cancérologie de l'Ouest (ICO) in Nantes, France. This database gathers individual retrospective data from all consecutive patients, male or female, ≥18 years, having started an anti-cancer treatment for an early breast cancer in our institution. Patient-related data are collected, including patient demographic characteristics, pathology, and outcomes. Treatment strategies are also recorded, including chemotherapy, targeted agents, endocrine therapy (ET), and radiotherapy (RT). Tolerance and side effects are not reported in the database and KI67 was not recorded in the database. In accordance with French regulations, the French data protection authority (CNIL, Commission Nationale de l'Informatique et des Libertés) authorized the database (authorization number 706825).
The multivariate model's validity for the proportional risks was verified. We used the multiple imputation method to keep the power and use all 1105 patients (109 events) without bias. Moreover, we tested this imputed model's robustness with a sensitivity analysis via maximum bias testing (either all missing data = 0 or 1). A continuous prognostic score was calculated from imputed model coefficients and applied to each of the 1037 complete cases. To obtain a binary classification score, optimal cut-off was assessed using the Contal and O'Quigley method, and the corresponding risk curves were traced. All tests were two-sided and the significance limit was set at 5%. All analyses were performed using SAS 9. 4 software (SAS Institute Inc. , Cary, NC, USA) and Stata Special Edition 16. 1 (StataCorp, College Station, TX, USA).
Patient Characteristics and Management Of the 1496 patients identified in the database, 1105 met the inclusion criteria. The main reasons for excluding patients were recurrence during ET (n = 10), HR-relapse (n = 10), and loss of follow-up immediately after the end of adjuvant ET (n = 371). The flow chart is shown in Figure  S1. The median follow-up from diagnosis was 111 months (IQR = 87-136). The median age at primary diagnosis was 62 years (IQR = 58-69). The patients' clinical and pathological features are shown in Table  1. Tumor size was mainly pT1 (77. 6%) or pT2 (20. 6%) while the Elston-Ellis grade was I, II, and III in 29. 6%, 52. 8%, and 12. 2% of patients, respectively. ER/PR status was distributed as follows: ER+/PR+ (76. 9%), ER+/PR-(22. 6%), ER-/PR+ (0. 5%). HER2 status was positive in 6.
The authors stated that endocrine therapy could be considered in patients with high Ki-67 LI (>20%) in the low CTS5 group and that the combination of CTS5 and KI-67 could predict more accurately the risk of late relapse. Ki-67 LI was not recorded in our database and was, therefore, not included in our model. Beyond the conventional clinicopathological factors included in our score or in CTS5, molecular signature may help to identify patients at risk of late relapse. These tests include the immunohistochemical 4 (IHC4) protein test, 21-gene Recurrence Score (Oncotype DX), PAM50 intrinsic subtype (Prosigna), 12-gene Recurrence Score (EndoPredict), two-component Breast Cancer Index (based on the molecular grade index and HOXB13: IL17BR), and 70-gene signature (MammaPrint)  [14, 24, [32] [33] [34] [35] [36] [37] [38].
The prolongation of AIs induced and increased odds of cardiovascular events (OR = 1. 18, 95% CI = 1. 00 to 1. 40, p = 0. 05), bone fractures (OR = 1. 34, 95% CI = 1. 16 to 1. 55, p < 0. 001) in a meta-analysis of seven clinical trials  [43, 44]. These latter side effects should be taken into consideration in patients with preexisting comorbidities or risk factors and deserve specific management. Our study has several limitations. The retrospective design may have induced some selection bias. However, the ICO BERENIS database included all consecutive patients from our institute. Secondly, validation in an independent population would be to implement the score more routinely in practice. Finally, patient compliance and tolerance cannot be evaluated and may impact the outcome. Several studies have highlighted patients' low rates of compliance with adjuvant ET and extending this treatment would involve only very motivated patients  [45].
About 0. 5 µL of proteolysis product solution was then deposited on top of the matrix surface and allowed to air-dry. Last, the sample was washed with 0. 5% aqueous TFA. Mass spectra were obtained using a Bruker (Bremen, Germany) Biflex MALDI-TOF mass spectrometer. The instrument was operated in the linear positive ion mode. The instrument was calibrated with a standard mixture of angiotentin II, ACTH 18-39 and bovine insulin. The instrument control and data processing were done with the Bruker built-in software on a Sun Sparc workstation. 39pep-2-M and α2-M samples were analyzed on an UltraFlex I mass spectrometer (Bruker Daltonics). A cysteine reduction was carried out for 10 min at 37 °C using 5 mM Tris(2carboxyethyl)phosphine hydrochloride prior to MS analysis of whole α2-M samples. Samples were diluted fifty-fold or hundred-fold in a solution of 4HCCA saturated in a solution of 66.
6% water, 33. 3% acetonitrile, and 0. 1% trifluoroacetic acid. Matrix-sample solutions were spotted onto a gold-plated sample probe using the ultrathin layer method  [48, 49]. Spectra were acquired in linear positive ion mode for α2-M samples and in reflectron positive ion mode for 39pep-2-M samples. Calibration of the instrument was performed externally using a neighboring spot with protein calibration mix consisting of apomyoglobin and cytochrome c or with pepmix I from Bruker. MALDI-TOF-MS spectra were processed and annotated using FlexAnalysis 3. 3 (Bruker Daltonics) and PAWS 8. 5 (ProteoMetrics) software, respectively.
ACKNOWLEDGMENTS This work was supported by  "Région Centre-Val de Loire" (PIRANA, SyMBioMS, MALDITOF)  and the  European FEDER program  ( 2017-EX002979 ). The authors thank  C. Epinette  ( INSERM U-1100 ) for excellent technical assistance and  Dr. P. Davies  and  Dr J. L. Humes  from  Merck  for the kind gift of hPR3.
INTRODUCTION The Human Phenotype Ontology (HPO) provides comprehensive bioinformatic resources for the analysis of human diseases and phenotypes, offering a computational bridge between genome biology and clinical medicine. The HPO was initially published in 2008  (1)  with the goal of enabling the integration of phenotype information across scientific fields and databases. Since then, the project has grown in terms of coverage, scope and sophistication, and has become a core component of the Monarch Initiative, allowing computational cross-species analysis  (2). HPO has also become part of the core Orphanet (3) rare disease database content. The Orphanet nomenclature of rare diseases, whose adoption has been recommended by the European Commission expert group of rare diseases for codification of rare-disease (RD) patients in health information systems (recommendation on ways to improve codification for rare diseases in health information systems: http://ec.
europa. eu/health/rare diseases/docs/ recommendation coding cegrd en. pdf), is being annotated with HPO terms in order to allow for deep phenotyping of RD in health records and registries. The description of phenotypic variation has become a central topic for translational research and genomic medicine (4-7), and 'computable' descriptions of human disease using HPO phenotypic profiles (also known as 'annotations') have become a key element in a number of algorithms being used to support genomic discovery and diagnostics. Here, we describe the latest improvements to the tools and resources being developed by the HPO Consortium and the Monarch Initiative, and provide an overview of external tools and databases that are using the HPO for translational research and diagnostic decision support.
Ontology The HPO has grown substantially since the first Nucleic Acids Research database article in 2014 (Version: 30 July 2013)  (8)  to the September 2016 release (Version: 3 September 2016). There are 1725 additional terms (10 088 in 30 July 2013 versus 11 813 in 3 September 2016, see Figure  1 ) and 2269 additional subclass relationships (13 326 versus 15 595). We obsoleted 82 HPO classes (44 versus 126). We have added 2024 textual definitions (6603 versus 8627) and 8063 synonyms (6265 versus 14 328). Logical definitions were constructed for an additional 1126 HPO classes, bringing the total number to 5717. These definitions refer to ontologies for biochemistry, gene function, anatomy, and others, and allow cross-species mapping by means of automated semantic reasoning. There are now 123 724 annotations of HPO terms to rare diseases and 132 620 to common diseases.
S. National Library of Medicine that integrates many standard biomedical terminologies  (18). In order to improve the coverage of phenotype data, the UMLS has now integrated the entire HPO starting with the 2015AB release. This enables an easy process to map HPO-encoded data to standard health-care terminologies such as SNOMED CT  (19). HPO has contributed to the establishment of the International Consortium of Human Phenotype Terminologies (ICHPT; http://www. ichpt. org) to provide the community with standards that achieve interoperability among databases incorporating human phenotypic features. The outcome is a set of over 2300 terms which should be incorporated in any terminology and which is fully cross-referenced with HPO terms. These terms are not arranged in a hierarchy and so can be mapped to or incorporated into any ontology.
The HPO project data are available at http://www. human-phenotype-ontology. org. Requests for new terms or other amendments can be made using the GitHub issues tracker https://github. com/obophenotype/humanphenotype-ontology/issues. Further information on HPOrelated publications and general announcements can be found on the HPO website at http://www. humanphenotype-ontology. org and on the HPO twitter feed @hp ontology.
DECIPHER now supports sequence variation and copy number variation in the nuclear and mitochondrial genomes. DECIPHER was an early adopter of HPO and is the platform through which data from the Deciphering Developmental Disorders study (DDD study) is shared  (24). At the outset of the project, the DDD study (www. ddduk. org) funded a week-long workshop to improve the HPO ontology by reducing redundancy of terms and improving coverage in the rare disease space. DECIPHER currently has 21,689 open-access patient records annotated with 60,521 HPO-encoded phenotype observations. PhenoTips (  15 ) is an open-source clinical phenotype and genotype data collection tool. It provides simple user interfaces to select and explore HPO annotations and suggest diagnoses from OMIM. Records within PhenoTips can be de-identified and pushed to PhenomeCentral  (16)  to participate in phenotypic and genotypic matching with other cases in PhenomeCentral and in connected databases through the Matchmaker Exchange.
PhenomeCentral makes use of HPO terms to measure semantic similarity between patient phenotypes and prioritize exome data using the Exomiser. At the time of this writing, PhenomeCentral contains 2640 matchable cases, of which 2059 have at least one HPO term, 172 are from the NIH UDP and 28 from the NIH UDN. Patient Archive (PA) (  2 ) is a clinical-grade phenotypeoriented platform for managing patient data; PA combines the richness of the HPO with highly intuitive user interfaces to aid the discovery and decision-making process in the context of clinical genomics. PA enables clinicians to use free text clinical notes as the starting point for structured HPOcentric patient phenotyping to support clinical diagnostics and care. To this end, an instance has been installed in the Western Australian Department of Health for clinical genetic use, both within and outside of, the Undiagnosed Diseases Program (UDP)--Western Australia; a clinical public   (25).
Numerous rare-disease research consortia are using HPO for patient annotation and analysis. In order to review and expand the HPO to better represent specific disease areas, the HPO consortium has conducted workshops with consortia including the European FP7 projects RD-Connect (28), EURenOmics and NeurOmics. Using advanced omics technologies, NeurOmics, an EU-funded translational research project, aims to characterize the causes, pathomechanisms and clinical features across ten major neurodegenerative and neuromuscular disease groups affecting the brain and spinal cord, peripheral nerves and muscle. EU-RenOmics is using high-throughput technologies to characterize new genes causing or predisposing to kidney diseases, concentrating on five groups of renal disease. RD-Connect is an integrated platform connecting databases, registries, biobanks and clinical bioinformatics for rare disease research that brings together multiple datasets on patients with rare diseases at a per-patient level.
Deep phenotyping of affected individuals is an essential component of these projects, and is being addressed by using the HPO as a mechanism for linking a computationally accessible phenotypic record with a genomic dataset. The projects performed a review of available ontologies at an early stage and concluded that the HPO was the most appropriate ontology for their gene discovery focus  (28). Both NeurOmics and EURenOmics performed mapping exercises in order to transform data items suggested by clinicians as essential items to record for each patient presenting with a particular clinical profile into HPO terms, and in most cases this mapping was able to produce exact matches to an already existing HPO term. Missing areas were then addressed in the expert workshops described above. Several of these projects make use of PhenoTips  (15)  in order to capture clinical data to a highly granular level through an interface that is user-friendly for clinicians.
Independently of the data entry mechanism, the use of the HPO means that the data generated by these consortia is fully interoperable with other datasets internationally. Currently, the RD-Connect platform contains ∼2000 exome, genome and panel sequencing datasets linked with HPO-coded phenotypic profiles from a range of rare diseases. The HPO was used within the EuroEPINOMICS-Rare Epilepsy Syndrome (RES) project to systematically assess phenotypes in patients with epileptic encephalopathies  (29, 30). A first analysis of clustering of epilepsy phenotypes was presented as a poster at the 2012 European Congress of Epileptology  (31) , while a more comprehensive analysis of the obtained HPO terms including exome sequencing data is currently underway. Clustering of patient phenotypes in 171 patients with epileptic encephalopathies identified a subgroup of eight patients with closely related phenotypes.
A review of manually curated phenotype data suggested these patients had a subset of Infantile Spasms with a good outcome. This preliminary analysis suggested that D870 Nucleic Acids Research, 2017, Vol. 45, Database issue the use of HPO terms in patients with epilepsy is worthwhile, given that the identified epilepsy phenotype was both homogeneous and clinically meaningful. The use of HPO terms for patients with epilepsy is challenging. In contrast to many other genetic disorders, the phenotypic features in epilepsy patients are dynamic and specific features such as a complex seizure semiology are often difficult to fully include in systematic phenotype ontologies. For example, a patient with simple febrile seizures may have self-limiting febrile seizures (FS), may have recurrent febrile seizures past the age of six years (FS+), or may develop the intractable, fever-related epilepsy of Dravet Syndrome over time.
The BRIDGE-BPD Consortium (48) used genome sequencing combined with HPO coding to identify a gain-of-function variant in DIAPH1 in two unrelated pedigrees with deafness and macrothrombocytopenia  (49). This finding was supported by Phenotype Similarity Regression (SimReg), an algorithm for identifying composite phenotypes associated with rare variation in specific genes  (50). HPO-based phenotype analysis also allowed matching of human phenotypes to mouse phenotypes by crossspecies analysis and thereby aided the discovery of a dominant gain-of-function mutation in SRC that causes thrombocytopenia, myelofibrosis, bleeding and bone abnormalities (51). The Matchmaker exchange (MME) platform provides a systematic approach to rare disease-gene discovery with a federated network of phenotype-genotype databases that enable data sharing and discovery of relevant data  (52, 53)  over a secure API  (54).
TRANSLATIONAL RESEARCH AND DIAGNOSTICS WITH HPO: ALGORITHMS AND TOOLS The HPO is a computational resource that allows algorithms to 'compute over' clinical phenotype data in an increasing number of contexts through a growing number of tools from the HPO Consortium and other groups (Table  2 ). The tools use the ontological structure of the HPO that allows individual terms to be associated with an information content, a measure of specificity  (55) , or with the underlying D872 Nucleic Acids Research, 2017, Vol. 45, Database issue The theory is defined but key details need to be defined and handled in the ontology computations HPO models biology, where diseases are caused by environmental factors, e. g. cancers --how can an environment ontology be included? Refinement The key data sets and definitions for the disease are identified and available but require 'translation' Theme based registry systems hold collections of data in other coding systems (registry-specific or ICD) --how can these be mapped onto HPO? Maturity The HPO framework is in place and productive results are being obtained, the HPO term set continues to evolve The HPO basics are in place and a set of Phenotypes in place --do we need more terms or do existing terms need modification? logical definitions of the terms, such that HPO terms can be linked to other resources such as model organisms  (56, 57).
PUBLISHING PROCESSES AND DATA EXCHANGE It is non-trivial to collect patient phenotypes reliably, whether retrospectively from existing medical data or prospectively. The overwhelming majority of clinical descriptions in the medical literature are available only as natural language text, meaning that searching, analysis and integration of medically relevant information is challenging. An important step to increase the amount and quality of phenotype data in databases is to obtain the relevant information from authors upon submission of articles. The journal Cold Spring Harbor Molecular Case Studies requires authors to select HPO terms for research papers that are displayed alongside the manuscript and that can be used to search journal content for other cases with overlapping HPO terms  (58).
HPO: AN ASSESSMENT BY THE NIHR RARE DIS-EASE INITIATIVES HPO is used as the system to capture of phenotypic information for the UK's National Institute for Health Research (NIHR) Rare Disease initiatives on projects such as NIHR RD-TRC (Rare Disease--Translational Research Collaboration, http://rd. trc. nihr. ac. uk) and the NIHR BioResource Rare Disease NIHR BR-RD. HPO is employed in all of these broad wide-ranging studies and includes data integration from a variety of sources such as multiple EHR systems, in a variety of locations and specialities. In some disease areas, for example, bleeding and platelet disorders, HPO has been the platform for new gene discovery and innovative research findings  (44) ; the advantage of HPO its support for statistical power associations across phenotypes across different diseases and in different branches of the HPO ontology.
The NIHR Rare Disease initiatives use a common infrastructure and clinical coding for the RD-TRC (56 studies), BR-RD (14 studies) and also in our contribution to the 100 000 Genome project (160+ targeted diseases, as mentioned above). This produces a large and diverse dataset with a growing 'data dictionary' containing terms mapped across different systems and coding schemes and includes clinically relevant signs outside of HPO--for example, lab test results or exercise questionnaires. In a short update, it is difficult to present the breadth of the contribution HPO makes to NIHR-RD research, which indeed is growing as more diseases are characterized and encoded using HPO. The HPO is now being employed in numerous NIHR-RD studies and it is anticipated that its use will be extended into all studies in which phenotype data are captured.