dataesr/scientific-paragraphs-categorization huggingface dataset:
Likewise, physicians did not talk about the Sales Visit Charter, suggesting that it remains relatively unknown more than 10 years after its adoption. Instead, they described a decrease in the frequency of medical visitors and in the size of the gifts offered to them. This decrease in the number and volume of gifts is confirmed by the analysis of the national "transparence-sante" database  [45]. This decrease was used by doctors as an argument to trivialize the impact of sales rep visits on their practice. Although we can welcome the decrease in gifts, this trivialization may contribute to ongoing GP ambivalence towards sales visits.
Acknowledgments First, the authors thank the study participants. They also thank the reviewers and the editor for valuable comments that helped to improve the manuscript, the  Office of International Scientific Visibility, UniversiteCote d'Azur , for proofreading,  Catherine Regent  (PhD) for proofreading and editing, and  Pr. Jean-Baptiste Sautron , (GP) Pr. Isabelle Milhabet (IM, psychologist),  Pr Isabelle Feroni  (IF, sociologist),  Dr. Carol-Anne Boudy  (GP),  Dr. Nicolas Hogu  (GP) and  Kamel Tribeche  (clinical psychologist) for their proofreading and their critical advice.
Vasicek Parametrization Equation (  77 ) above is general and can be used with a variety of interest rate models. However, one needs to provide a specification for the parameters E, Ê, and v, where the latter two quantities are not quoted quantities that can be easily retrieved from financial databases. I choose to implement a Vasicek model because it is simple, little-parametrized, its parameters are financially intuitive, and the model allows for the possibility of negative interest rates. As shown below, the main interest rate quantities that need to be calibrated by actuaries to feed the formulas are the volatility σ and the force of mean reversion a of the short term rate. These quantities can be easily calibrated using the Vasicek formula for the yield curve, together with, e. g. , Bloomberg data.
Tis risk is measured by a VaR based on a one-year horizon and a 99. 9% confdence level. Te computing frequency is weekly, and the DRC capital requirement is equal to the following: KDRC � max 1 N 􏽘 N i�1 DRC i , DRC N+1 ⎛ ⎝ ⎞ ⎠ ; N � 12. (2) We should calibrate four components and model to implement the loss function. Te frst component is the obligor's correlation. Initially, the regulator allows using the credit spread or the historical listed equity price data. Tese historical data must include at least 10 years and the stressed period, as defned in the ES model. Te chosen liquidity horizon is the one-year liquidity horizon, and the minimum for the equities is set at 60 days. Tese data must give a higher correlation for portfolios, including short and long positions. On the other hand, a low correlation is assigned to the portfolios that contain only long exposures.
Banks could also use the external rating provided by rating agencies (e. g. , S&P, Fitch, or Moody's) to estimate PDs. In this case, they must defne the priority ranking choice. Te third component is the Loss Given Default (LGD) model. Te LGD model must catch the correlation between recovery and systematic factors. Te model must be calibrated based on IRB data if the institution already has a homologated model. Te historical data should be relevant to get accurate estimates. All LGDs must be foored to zero, and the external LGDs could be used, respective to some defned ranking choice. Te fnal component is the Jump to Default (JTD) model. Te JTD model must catch each obligor's long and short positions. Additionally, the set assets must contain the credit (i. e. , sovereign and corporate credit) and the equity exposures.
We deem a set of 1,481 issuers within a 10-year historical spread. Our population contains 69 sovereigns in six regions and 11 industries. Terefore, we build a set of systematic factors orthogonal by subsets, which gives us the block of zeros in building a ρ F with 20 × 20 as the dimensions. Te relevance of our model could be measured by comparing the implied correlation with the historical correlation. We assume that we use the log return of historical data and note ρ H , the Pearson historical correlation between obligors. First, we propose plotting the density function of ρ H and ρ I to see if the implied distribution fts with the historical one. Figure  3  shows that the implied density is close to the historical density, and both look very similar. However, the plot of densities is not enough to measure ft.
Materials and methods PRM platform aggregates Electronic Pharmacy Records (EPR) data from French medical centers (PRM centers) to build retrospective cohorts of patients treated with injectable cancer drugs in a hospital setting. Data extracted on January 1 st , 2020, from breast cancer (BC) patients who received trastuzumab, trastuzumab emtansin or pertuzumab since January 1st, 2011, and from lung cancer (LC) patients who received bevacizumab or atezolizumab since January 1 st , 2015, enabled recovering their injectable cancer drugs history from diagnosis date until December 30 th , 2019, and served as dataset for assessment.
Results 123 PRM centers provided data from 30,730 patients (25,660 BC and 5,070 LC patients respectively). Overall, 20,942 (82%) of BC and 4,716 (93%) of LC patients were analyzed. Completion rate was above 98% for patients characteristics, diagnostic and treatment related data. PRM centers cover 48% and 33% of BC and LC patients in-hospital therapeutic management in France, respectively. Distribution of BC and LC patients therapeutic management, by medical center category and geographic location, was similar in PRM centers to all French medical centers, ensuring the representativeness of the PRM platform.
To date, RWD have played a minor role in the P&R process in France, mainly because their implementation is hindered by: 1/ hurdles to efficiently capture health data (data availability and quality), 2/ restrictive data sharing policy, 3/ lack of sustainability in data collection and analysis, and 4/ difficulties to generate robust RWE  [15, 16]. Having an appropriate data infrastructure able to capturing good quality and representative data in a sustainable manner, and generate promptly on demand robust RWE is an essential requirement to increase the use of RWD at each step of the health technology assessment. In contrast to most available databases in France, PRM platform is designed to process on demand electronic pharmacy records (EPRs) data informing payers decisions, and can provide results in a few weeks with sufficient coverage and representativeness to produce robust and scalable RWE.
Electronic Pharmacy Records (EPRs) are widely implemented and used in French medical centers for over twenty years by physicians and pharmacists in their routine practice for prescription, dispensation and administration of injectable cancer drugs. EPRs contain patient characteristics, diagnostic and treatment related data. Initiated in 2014, the PRM program aims at aggregating EPR data of breast and lung cancers patients treated in French medical centers. This article describes the Personalized Reimbursement Model (PRM) program methodology, limitation, achievement and perspective in using real-world data of cancer drugs use to improve and personalize drug pricing and reimbursement in France.
Description of PRM platform PRM platform aggregates EPR data from 130 public and non-public French medical centers involved in the PRM program (PRM centers) in a centralized database (PRM database). Each data extraction from PRM database enables building retrospective cohorts of patients who received at least one injection of a cancer drugs of interest (trastuzumab, trastuzumab emtansin, pertuzumab, bevacizumab or atezolizumab), and recovering their injectable cancer drugs history (drugs on the T2A-list and outside the T2A-list) from diagnosis date until data extraction date. Extracted variables mainly include patient characteristics, diagnostic and treatment related data. All variables extracted are presented in Table  1. PRM centers extract, pseudonymize and deposit EPR data on PRM platform provided by a third-party ISO-27001 certified hosting provider Advanced Schema.
In accordance to the authorization given by the French Data Protection Authority (CNIL), only the Roche data processor, Advanced Schema, is described as the recipient of the dataset. The Information Notice given to the patients does not provide a specific information about sharing the data with other third parties. In this context, the sharing of the pseudonymized dataset is not permitted. However, the dataset can be shared for legal obligations with authorities. PRM program is compliant with European and French legislation regarding health data, data protection and patient information. This work was supported by Roche. Roche was involved in the statistical analysis.
Data management The first step of data management consists in codifying and standardizing unstructured free text data extracted from different EPR systems (S1 Table ). Data are then pooled and stored on a dedicated and secure database server. The second step of data management consist in removing patients with: 1/ treatment sequences having unknown start date, i. e. the start of the treatment sequence is truncated, most likely due to an EPR software change, which does not allow to retrieve complete cancer drugs history, 2/ inconsistent cancer drugs name, i. e. brand name that does not match the International Nonproprietary Name (INN), 3/ inconsistent drug administration, i. e. two administrations of the same injected cancer drug on the same day, leading to the suspicion of a coding error, 4/ two or more concomitant primary cancer diagnoses reported, and 5/ administration of cancer drugs leading to the suspicion of the treatment of another concomitant cancer.
has been validated and is regularly updated by an independent scientific committee involving oncologists, pharmacists and statisticians (S3 Table ). Cancer drugs that are used in current practice to treat BC or LC, including cancer drugs used off-label or outside guidelines, are not on this list and are therefore captured by the PRM platform. The third step of data management consists in applying algorithms to complete or correct data relative to disease stages and treatment lines when they are initially incomplete or missing. These algorithms consist in: 1/ retrieving relevant information from different entries in the database, 2/ checking information consistency over time and 3/ extracting relevant features to complete or correct data. These algorithms are detailed in S4 Table and their relevance has been supervised by the independent scientific committee.
Assessment of completeness, coverage rate and representativeness The potential of PRM program to capture real-world data of cancer drugs use to help inform drug pricing and reimbursement decisions in France, is assessed through three criteria: data completeness, coverage rate and representativeness of PRM database. As an example in this article, data extracted on January 1 st , 2020 enabled building cohorts from BC patients who received trastuzumab, trastuzumab emtansin or pertuzumab between January 1 st , 2011 and December 31 st , 2019, and LC patients who received bevacizumab or atezolizumab between January 1 st , 2015 and December 31 st , 2019. Completeness is measured by the proportion of available data for each variable in the PRM analyzable population (i. e. completion rate), after applying data management algorithms for disease stage and treatment line variables.
Quality controls are performed to check that data management algorithms are correctly applied. For each algorithm, a patient sample of approximately 60 patients is selected and reviewed on the user interface (S1 Fig) presenting patient treatment related data, disease stage and treatment line before and after the application of data management algorithm to validate the correct application of the rule. For variables with a missing data rate higher than 30% (completeness below 70%) no imputation technique are performed and the variables are not considered for analysis. Coverage rate at national level is controlled by comparing the numbers of BC and LC patients treated in PRM centers to those treated in all French medical centers through the French National Hospital database (PMSI). We assumed a target coverage rate of 33% of French BC and LC patients treated in PRM centers to be sufficient.
To ensure representativeness of BC and LC patients therapeutic management at national level, PRM centers were selected through a randomized stratified sampling method on medical center category and geographic location (region). This methodology should ensure that the patient characteristics in the PRM database are comparable to those in the national population. The four medical center categories are: 1/ Comprehensive Cancer Centers (CCC), 2/ University Hospitals (UH), 3/ General Hospital (GH) and Non for-profit hospitals (NFPH), 4/ Private Hospital (PH). GH and NFPH were grouped based on similar practices and patient profiles. While more data are needed to describe in a more comprehensive way the practices and patient profiles in the different types of medical centers in France, a pragmatic approach based on healthcare professionals' feedback was assumed.
Discussion As of January 1 st , 2020, PRM platform enabled capturing RWD from EPR systems among 123 public and non-public French medical centers covering more than a third of BC and LC patients in-hospital therapeutic management in France. PRM database is populated with 30,730 BC or LC patients EPRs data, filled in by physicians in their routine practice for cancer drug prescription, and reviewed by pharmacists for cancer drug dispensation, which enables having a high completeness and correctness for patients characteristics, diagnostic and treatment related data. Moreover, effective data management algorithms improve data quality for disease stage and treatment line. Patient in-hospital injectable cancer drugs history is linked to patient characteristics, diagnostic, disease stage and treatment line, over time.
This enabled the computation of RWE of trastuzumab, trastuzumab emtansin, pertuzumab, bevacizumab or atezolizumab use, based on: 1/ target population and patient profiles that are likely to be treated in real-world setting, 2/ off-label use and compliance with guidelines, 3/ treatment indication, especially for label extension and combinations with other treatments, 4/ involvement in clinical trials or in early access programs (ATU) and 5/ benefit of the drug based on proxy indicators such as the number of drug injections or treatment duration. Equitable distribution of BC and LC patients treated in PRM centers, by medical center category and geographic location, ensures the representativeness of the RWE at national level. Data extractions have been honed for several years and are increasingly automated avoiding time lag between.
Limitations Patient characteristics, diagnostic, and treatment related variable have high data completeness and correctness since they are mandatory for cancer drug prescription and double-checked by physicians and pharmacists in their routine practice through the EPR software, except for disease stage and treatment line that are provided at physicians discretion. Data management algorithms were developed to infer these variables from available data in PRM database. Data management processes and algorithms are controlled and continually improved in order to adapt to disease therapeutic management updates, supervised by the independent scientific committee. However this may still be insufficient and a validation with medical charts or other data sources would be of interest. Comparison with other RWD sources would enrich our understanding of the PRM program validity and representativeness.
We attempted to compare the patient characteristics of PRM cohorts with other patients cohorts or registries. Nevertheless, it seems that this is to date barely feasible. For methodologically valid comparison, PRM database should be compared to 1/ a database which allows to retrieve patient cancer drugs history at a national level, 2/ ensuring representativeness of BC and LC patients therapeutic management by medical center category and geographic location, 3/ for a similar population as PRM (patient having received at least one Roche treatment) and 4/ covering a period consistent with the January 2020 PRM extraction. To our knowledge such a patient's cohort or registry is not available in France. A comparison with national medico-administrative databases might be considered, even if the possibility of isolating comparable patients cohorts remains limited by the difficulty of identifying, beyond a precise diagnosis, disease stage, treatment lines and their therapeutic indications, all the more so as treatments outside the T2A-list are not available.
Such a study could nevertheless be an added value for the program and should be considered in a near future. Other variables such as metastasis's location, oncogenic drivers, treatment response and treatment discontinuation, are poorly completed by physicians because their collection is not mandatory in EPRs systems, while available in patient medical records. These variables are not currently used to generate RWE. Close collaborations are established with medical centers involved in PRM program to improve data quality through specific improvement actions and incentivization. For each extraction, a personalized feedback is provided to PRM centers through reports and dashboards to monitor their activities. Aggregated data at regional and national levels are also shared for comparison purposes and as incentives to improve data quality.
PRM platform aggregates EPRs data and enables recovering patients injectable cancer drugs history from diagnosis date until data extraction date. Nevertheless, in some situations, this drug history may suffer some gaps. One situation is the use of oral cancer drugs and nonhospital-based injected drugs that are rarely recorded in the EPRs since not mandatory to be delivered to patients. This causes missing treatment sequences, sometimes difficult to identify. EPR software are gradually evolving to capture oral treatments. Still, healthcare professionals need to compel themselves to complete EPR with oral treatments and non-hospital-based injected drugs. Another situation that may lead to missing treatment sequences is related to patient change from medical center. The lack of unique patient identifier does not allow to link EPRs data across medical centers.
Perspective In recent years, the PRM program enabled building a platform aggregating up-to-date, good quality and representative RWD of in-hospital injected cancer drug use  [18]. PRM program enabled setting the first Performance-Based Risk-Sharing Arrangement (PBRSA) based on PRM data between the French Healthcare Products Pricing Committee (CEPS) and Roche for the first indication of atezolizumab cancer immunotherapy in second line advanced Non-Small Cell Lung Cancer (NSCLC) in February 2019. This PBRSA is based on paybacks to the French National Health Insurance according to the proportion of patients not benefiting from atezolizumab, calculated through treatment duration in real-world setting. Among the five cancer drugs tracked in the PRM program, atezolizumab was the only drug that has benefited from a PBRSA.
The CEPS advocates over the last decade that PBRSA is of weak interest because it did not contribute enough to the control of uncertainties regarding drug effectiveness that remain when assessed by health authorities. The objective of the PRM program is to provide on demand, robust real-world data to generate PBRSAs based and precise, co-constructed and shared indicators allowing CEPS to regain interest in this type of contract. A recent collaboration with Unicancer (French Hospital Federation bringing together the 18 French CCC), allowing to co-build the Oncology Data Hub which capitalizes on the complementary expertise and database of the ESME platform (the most important oncology RWD source in Europe) and the PRM platform, opens new perspectives. Extending PRM Program to other drugs or to other types of cancer does not seem to present major difficulties.
Implementation costs are reduced compared to traditional observational studies and workload is limited since PRM program aggregate EPR data that are provided by physicians and pharmacists, in their routine practice. Nevertheless, the extension of PRM program to other drugs or indications may require the involvement of more PRM centers to ensure acceptable representativeness and coverage rate by PRM database and the endorsement of the French Data Protection Agency (CNIL). Further improvements would involve monitoring drug safety and effectiveness parameters  [19]. Such developments require better capturing performance status, comorbidities, oncogenic drivers, treatment response, patient reported outcomes, adverse events or death related data. The most efficient option is certainly to enrich the PRM database with data already available in other French databases.
France has national exhaustive medico-administrative databases, such as The French National Health Data System (SNDS), offering a long follow-up and little patient drop out, allowing particularly to follow the patient's hospital journey (i. e. hospitalization for cancer relapse or complication), consumption of outpatient cancer drugs (i. e. oral cancer drugs) and the cause and date of death  [20]. Other health database such as French Shared Medical Record (DMP), Communicating Cancer file (DCC) or cancer registries would allow to capture clinical, oncogenic driver and treatment response related outcomes. Enriching the PRM database with such data currently faces three main hurdles: 1/ The difficulty to merge PRM database with French medico-administrative databases, such as the SNDS, due to data privacy issues.
Even though a unique patient identifier to centralize patient data is available in France, its permanent access is regulated by law and restricted to public institutions. Its use for research, on an ad hoc basis, is very limited, 2/ The lack of interoperability across health information systems. Some medical centers have up to 300 different information technology (IT) applications; and the lack of interoperability between software, as well as services and digital tools bring additional hurdles, 3/ The lack of data quality  [21, 22]. The DMP lacks data completeness, since it is not routinely used in clinical practice. The DCC, a common record that facilitates Decision-making Multidisciplinary Team Meetings (RCP), is difficult to process into a structured database since its content is mainly populated with free text.
Contexts where multiple sources of information of varying quality from multiple stakeholders need to be balanced to inform decision-making. However, this may change with the Health Data Hub initiative that aims at concentrating and linking existing health databases in France to facilitate their use  [23]. Furthermore, Social Security Financing Act Project (PLFSS) for 2021 reports a public healthcare investment plan with commitment to invest €1. 4 billion over the next three years to modernize digital healthcare tools and address interoperability issues between healthcare IT systems. To date, there is still a low acceptability of RWE where the outcome of interest is effectiveness. Methodological challenges arise from the fundamental fact that RWD are not collected in a research perspective and therefore suffer from multiple biases and confounders  [24, 25].