dataesr/scientific-paragraphs-categorization huggingface dataset:
The DMO data collected by the FRB! software were kept to a minimum to deliver an efficient data collecting tool and were determined by a steering committee of retinal experts to focus on tracking valid and relevant outcomes and emerging treatments in routine clinical practice. Data fields of patient details, baseline visit and follow-up visits are summarized in Table  2. Data, which are entered at each clinical visit, include key outcomes, including the visual acuity (best of uncorrected, corrected, or pinhole), treatments given, if any, ocular adverse events and other ocular procedures such as cataract surgery. Additional information is collected at the baseline visit, such as patient demographics and previous treatments received. Treatment decisions, including choice of drug and re-treatment criteria, are at the clinician's discretion in consultation with the patient with no interference by the FRB! investigators, thereby reflecting real-world practice.
The screen to collect data from baseline visits was designed to be filled out in <30 s and follow-up visits in <15 s. The web-based software displays visual acuity, central sub-foveal thickness and respective treatments given over time in an interactive, user-friendly graph. Additional details, such as a change in the number of letters read at the selected visit compared with the previous visit and to the baseline visit can be revealed by hovering over various features with the help of the computer mouse (Fig. 2 ). A detailed summary of the key insights produced from the FRB! registry on the real-world treatment outcomes of nAMD has been published previously  (Nguyen et al. , 2021). In Section 3, we will similarly describe the numerous findings on DMO by the FRB! investigators. The continued growth and expansion of the FRB! registry places it in a prime position to answer essential questions regarding the real-world treatment of DMO, including long-term outcomes of current therapies, and the real-world effectiveness of the next generation of treatments such as brolucizumab, faricimab and high-dose aflibercept.
Guidelines and principles for analysing and reporting RWE Our FRB! group and the task force of the international society for pharmacoeconomics and outcomes research (ISPOR) and the international society for pharmacoepidemiology (ISPE) have provided similar good practice guidelines to analyse, interpret and report RWE  (Berger et al. , 2017; Teo, 2023). The STrengthening the Reporting of OBservational studies in Epidemiology (STROBE) statement guides transparent reporting of observational studies and should be applied to RWE  (von Elm et al. , 2008). Prior to analysing RWD, researchers should formulate an "a priori" project proposal in which clinical questions and hypotheses are set. A statistical analysis plan (SAP) is then developed to choose the appropriate statistical method dependent on different factors, such as study design, primary outcome and dataset selection (inclusion and exclusion criteria, comparator group and sample size power).
The proposal and SAP should be discussed by the study group for improvement and, if possible, publicly published on the study group site when finalised. Based on the SAP, the data scientist performs the dataset analysis in a subsequent stage. Using appropriate statistical methods, issues related to heterogenicity of baseline characteristics or treatment exposure or loss to follow-up selection bias should be addressed  (Teo, 2023). Some of those methods are developed in further sections of the review. The final stage is interpreting the statistical analysis results to present their findings and conclusions in a publication. Authors should clearly acknowledge the limitations of their analysis and which methods were used to mitigate them. It is also recommended to discuss the generalizability of their results to the population beyond their dataset and provide a summary of the essential findings and their clinical utility  (Berger et al.
Mean value substitution introduces severe statistical problems since it artificially reduces the variance of the imputed variable since all patients with missing data will have the same value. Multiple imputations is a much more effective method as it accounts for statistical uncertainty in the imputations by imputing several plausible values. Imputation can be achieved by sampling from an imputation model such as chained equations, the analysis (e. g. , multivariate regression) performed on each of the imputed datasets and the results subsequently pooled  (Azur et al. , 2011). In the case of incomplete longitudinal data, mixed-effects regression models can be used to estimate treatment effects via maximum likelihood without the need for imputation  (Molenberghs et al. , 2004).
Audits of data quality measurements As noted above, the utility of registries and RWE may be limited by the quality of data collected. To improve the quality of the data and therefore the use of RWE as a credible tool for tracking outcomes in ophthalmology, retrospective audits can be performed. The level of agreement between the RWD collected and the patient files can be ranked using Cohen's kappa (κ), intraclass correlation coefficients and positive and negative predictive values (V. Nguyen et al. , 2020). An audit carried out on data collected by FRB! performed a retrospective analysis of data recorded in the database compared with the doctors' own clinical records. The authors looked at sex, birth year, previous treatments received, treatment, and visual acuity. They found an overall error rate of 3.
5% with VA being the most common discrepancy (5. 1%). The discrepancy in VA was thought to be due to incorrect conversion of VA scales and failure to record the best recorded VA (best of corrected, uncorrected, or pinhole). Overall, this audit supported the high quality of RWD contained in the Fight Retinal Blindness! database by showing an overall very good accuracy (≥95% for all fields examined) (V. Nguyen et al. , 2020).
What have we learned from the real-world data? A large amount of RWE has been published on DMO, much of it of poor quality  (von Elm et al. , 2008; Ziemann et al. , 2022). We have mainly focused on significant findings from the FRB! registry and the French Medical-Administrative Database for Epidemiology and Safety in Ophthalmology (EPISAFE) program related to DMO and the safety of intravitreal therapy. Table  4  summarized main results and findings discussed in this section.
Countries starting treatment with higher VA with less strict reimbursement policies have better visual outcomes over time  (Mehta et al. , 2022). The impact of local reimbursement policies can be better understood by comparing international results. Furthermore, ethnicity representativeness is an issue in RCT data  (Kaakour et al. , 2022). Collecting ethnicity data in an international unselected RW database can also give useful information on treatment effectiveness and safety in those patients in daily practice. Baseline ocular characteristics such as DR severity, panretinal photocoagulation status, previous DMO treatment and DMO location and duration are crucial data to collect for the interpretation of results. Treatment naïve patients with less severe DR and shorter period of centre-involving DMO are likely to have higher visual and anatomical improvement with treatment  (Bressler et al.
Safety Intravitreal injection has been one of the fastest-growing procedures in medicine because of expanding indications for ocular diseases and the ageing of the population. It is a relatively safe procedure, but serious complications may occur locally, including rhegmatogenous retinal detachment, cataract formation, retinal artery occlusion, non-infectious or infectious endophthalmitis, and potential systemic adverse events, such as cardiovascular events. Fortunately, these adverse events seem to remain relatively rare. RWD have already shown their value in detecting them compared to RCTs that are not designed and powered enough to detect those rare safety concerns. The FRB! registry and the French Medical-Administrative Database for Epidemiology and Safety in Ophthalmology (EPISAFE) program have gathered interesting safety evidence on intravitreal drug therapy in ophthalmology  (Daien et al.
, 2018). Similarly, using the French medical-administrative database (Système National d'Information Inter-régime de l'Assurance Maladie [SNIIRAM]), Baudin et al. recorded 444 endophthalmitis cases after 1 811 977 intravitreal injection (1 per 4082 injections) in patients treated for retinal diseases in France from 2012 through 2015 with a crude incidence of 0. 025%  (Baudin et al. , 2018). The incidence of infectious endophthalmitis after corticosteroid injections (0. 0667%) was 3-fold higher than VEGF inhibitor injections (0. 0204%). One interesting finding was that the prefilled ranibizumab syringe (available only for ranibizumab at that period) decreased the rate of endophthalmitis after injection by 40% compared with the nonprefilled ranibizumab syringe confirming the hypothesis that fewer manipulations during intravitreal injection and preparation of prefilled syringe in a controlled environment was safer.
, 2012; Yu et al. , 2011). Administrative medical databases are potent sources of RWD to study associations involving rare events due to their larger sample size. Preliminary unpublished analysis of the French National Health Information database (Système National des Données de Santé  [SNDS] ) and covering all types of VEGF inhibitors introduced from 2010 to 2016 in France, compared the rate of cardiovascular morbimortality events in patients treated for retinal diseases (n = 330 000) at 28 days and 2 years after the introduction of an intravitreal VEGF inhibitor with matched control patients undergoing cataract surgery (n = 996 000). There was no evidence of increased risk of overall cardiovascular morbidity and mortality after the introduction of IVT anti-VEGF therapy at 28 days (0. 24% versus 0.
, 2018; Fossum et al. , 2018; Introini et al. , 2012; Jouve et al. , 2015; Naderan et al. , 2021; Peracha and Rosenfeld, 2016; Petrou et al. , 2010; Polizzi et al. , 2015; Polizzi and Mahajan, 2015; Rosen et al. , 2009; Sarhianaki et al. , 2012; Sarmad and Lip, 2016; Sullivan et al. , 2014; Tarantola et al. , 2010; Wu et al. , 2010). The EPISAFE program carried out a 10-year retrospective French nationwide study to report the incidence of obstetric and neonatal complications in pregnant women treated with intravitreal VEGF inhibitor and corticosteroid using the French health insurance database  (Ben Ghezala et al. , 2022). Two hundred twenty-eight VEGF inhibitor or corticosteroid injections were administered to 139 women during pregnancy or in the month preceding pregnancy between 2009 and 2018, of which 93 had VEGF inhibitors, 39 corticosteroids and 7 both drugs.
Big data The term "big data" is defined by a dataset that is so large that it makes the analysis impossible with classical methods and that requires complex statistical tools. It is known as the 3Vs (volume, velocity and variety), completed by a fourth item, veracity, and sometimes a fifth one, variability. The European Union commission recently defined big data as the following: "Big data in health encompasses high volume, high diversity biological, clinical, environmental and lifestyle information collected from single individuals to large cohorts, about their health and wellness status, at one or several time points"  (Auffray et al. , 2016). Many datasets have been proposed using either: • Medical administrative database (Medicare or Medicaid in the US, the French national healthcare system data claim database named "Système National des Données de Santé (SNDS)" in France or Taiwan's national health insurance research database in Taiwan  (Chen et al.
, 2011) ). • Registries using EMR (IRIS registry or UK electronic medical records) and conventional registries (Fight Retinal Blindness registry) as detailed earlier  (Denniston et al. , 2019; Gillies et al. , 2014b; Nguyen et al. , 2021; Parke et al. , 2017). The IRIS Registry uses a software tool capable of automatically extracting data from a practice's EMR system in the US. The FRB registry needs manual input but gathers data from more than 20 countries worldwide. • Cohort studies can also provide very interesting results by collecting clinical and imaging data from population-based studies, the Constance cohort involving 220 000 individuals  (Zins et al. , 2010) , the UK biobank including 500 000 individuals  (Patel et al. , 2016) , the European Eye Epidemiology (E3) consortium cohort that gathers more than 15 European population-based studies involving more than 100 000 individuals  (Delcourt et al.
, 2018; Mauschitz et al. , 2018) ). All data provided by these studies are interesting and complementary. The main weakness is that associations found in big data studies do not necessarily mean causality. One should remember that a result can be linked to multiple, varying or, even worse, unknown factors  (Coleman, 2015). The ultimate goal is to combine both sources of information, for instance, registries and medico administrative databases, using different methods, which will be discussed further below. Although strongly controlled by the general data protection regulation (GDPR), the data can provide information on systemic diseases in patients, that are not always exhaustively collected by registries, as well as social situations and individual data (area of residence, environmental conditions …).
Linking datasets and harmonising data collection for dataset software interoperability Linking large-scale datasets is one of the main goals of future developments in RWD. One aim is to establish larger datasets where information from several resources, i. e. several registries, is aggregated to run large-scale analysis. This will better address questions about how a medication is used in real-world practice, where it is delivered and how it is delivered -and what the outcomes associated with treatments are. This might be relevant when outcomes across countries, different reimbursement systems and areas with different access to healthcare are to be compared. Linking imaging to clinical registry outcomes would enable greater data-driven insights and potentially allow more personalised medicine.
Data mining technologies One way to gather RWD is by using data mining technologies. Data mining software trawls through existing medical records and examines information about patients that are registered electronically. The software then tries to identify fields/variables and the information/values they contain. This can be done by e. g. identifying visual acuity readings or using codes (e. g. ICD 10), or in some cases information from free text fields. The advantage of this approach is that a huge number of data fields can be analysed and extracted from patient records. This mining method is also less expensive than other approaches. However, the quality of the information collected by data mining technologies is generally inferior to other data sources. Incomplete datasets, either because the information was not recorded or it is there and could not be read using a data mining approach, are a particular problem of RWD.
2 million otherwise eligible patients. This is not unusual, as in routine clinical practice, documentation and postoperative standard procedures in routine clinical practice can vary significantly across institutions, countries and healthcare systems. Visual acuity is not always recorded in a notation that would be needed for an analysis. In another article from 2018 addressing visual acuity outcomes in patients receiving VEGF inhibitors to treat nAMD, approximately 1/3 of eyes included were missing visual acuity values at baseline or one year of treatment  (Rao et al. , 2018). Again, this does not imply that the doctors did not care well for their patients; it merely reflects what this approach to collect data produces. Another issue in data mining technologies is when non-uniform ways to report visual acuity or when unique abbreviations are used to document findings.
The great advantage of this approach is that the data quality is generally very high. Data quality is higher if key fields are mandatory. If this principle is followed, practically all key data on every patient entered into the EMR will be transferred to the registry. The disadvantage of this approach is that it is cost-and labour-intensive to set up due to the adaptations to the local EMR that are required. This usually has to be done in conjunction with hospital or practice IT. If any changes are made to the data that the registry wishes to collect, the local EMR needs to be adapted again. Another potential disadvantage is that participation in several registries can become very tricky. An example of this approach is the Swedish macula register or the FRB! registry, which track fewer patients but provide higher quality datasets than the IRIS registry.
It will be up to the user to make sure that the data collected primarily in clinics or practices will be of good quality (e. g. by introducing mandatory fields or other quality assurance measures) to avoid the lower quality data produced by data mining. The establishment of a standard such as FIHR will allow not only linkage of registries and EMRs but also patients access to their health information, thereby involving them more deeply in the therapeutic process which will improve treatment adherence and, in turn, outcomes. Apple has implemented FIHR in their smartphone's health app (information at https://www. apple. com/healthcare/health-records/), and Google published their Android FIHR software development kit (information available at https://github. com/google/android-fhir) providing the basis to access health-related information on a smartphone.
The three main collaborations operating the current generation of imaging atmospheric Cherenkov telescopes (IACTs: H. E. S. S. , MAGIC, VERITAS) publish their gamma-ray data in different formats and repositories. Extragalactic sources are highly variable at very-high energies (VHE,  > 100 GeV), and a unified repository would enable joint analyses of collections of extragalactic VHE spectra. To this aim, we have developed the Spectral TeV Extragalactic Catalog, STeVECat, which gathers high-level products of IACT observations from 1992 to 2021. We selected all publications in journals referenced in TeVCat that presented archival spectra with at least two points. We compiled the corresponding spectral data and formatted them following the convention adopted in available public repositories (GammaCat and VTSCat).
In addition to spectral points with associated physical units, we provide meta-data featuring observation periods, livetime, excess counts over background and significance, as well as the coordinates, types and redshifts of the sources whenever available. STeVECat combines observations from 173 journal publications, compared to 72 in the previous reference compilation of extragalactic gamma-ray spectra  (Biteau & Williams, 2015). STeVECat is the most extensive set of VHE extragalactic spectra collected so far, with 403 spectra from 73 sources. The full catalog can readily be loaded with GammaPy, the Science Analysis Tool selected by the Cherenkov Telescope Array Observatory. Our compilation efforts enable population studies of extragalactic gamma-ray sources, studies of the GeV-TeV connection, and studies of absorption on the extragalactic background light.
The H. E. S. S. Collaboration, observing in Namibia's Khomas Highlands, provides supplementary data to their published spectra. The MAGIC Collaboration, observing in La Palma, Spain, provides access to a fits file repository containing data from observed spectra. The VERITAS Collaboration, observing in Arizona, USA, recently established a dedicated online repository (VTSCat, see  [6] ) to gather all of their published data in ECSV format. The latter is a format developed by the Astropy project1 to store astronomical data as well as their associated metadata. However, some publications are missing from those databases, and some spectra are reported with fluxes corrected for the EBL-induced absorption instead of the fluxes observed by the instruments. Efforts have been made to create a unified repository of extragalactic VHE spectra, like BW15 and GammaCat, but these repositories are missing the most recent observations.
STeVECat data 2. 1 Data collection STeVECat is a database of extragalactic VHE spectra. To compile the data in STeVECat, we searched journal publications referencing extragalactic sources in the TeVCat2 database. We selected all publications providing TeV energy spectra with at least two spectral points. This 1Astropy: Python library for astronomy, see https://www. astropy. org/ 2TeVCat: online catalog for TeV astronomy; see http://tevcat2. uchicago. edu/.
PoS(Gamma2022)171 STeVECat, the Spectral TeV Extragalactic Catalog amounts to 173 publications, as opposed to 72 in BW15. In some of those publications, the authors reanalyze previously published data; in such cases, we only report the most recent analysis in STeVECat. Lucas Gréaux 150°120°90°60°30°GC 330°300°270°240°210°l -75°- 60°- 45°- 30°- 15°0 °15°3 0°4 5°6 0°7 5°b Each individual spectrum is stored in a single ECSV file, and the files are ordered according to their associated publication. The spectral data is saved as a table-like structure, with columns corresponding to the energy bin center and the associated flux and flux error. We retrieved the spectral points for each selected publication from the publishing collaboration's repository when possible; otherwise, we contacted the relevant authors or digitized the figures.
Additional columns appear when applicable, corresponding to the upper and lower edges of the energy bins, the negative and positive errors on the flux, and the upper limits on the flux. The metadata, which is saved as a YAML-like structure, contains physical units associated with each column as well as the name of the observing instrument, the observation period in terms of start and end modified Julian date (MJD) as well as common calendar dates, the name and id of the observed source, the reference to the publication and a selection tag (see section 2. 2). The livetimes, excess counts over background, and significance also figure in the metadata when they were provided in the original publication. STeVECat gathers 403 spectra from 73 extragalactic sources which are shown in Fig. 1  from publications between January 1992 and December 2021.
Reliability of STeVECat We performed checks on the extracted data to ensure the reliability of STeVECat. We compared STeVECat data to data from GammaCat and VTSCat to ensure that STeVECat was complete for spectra observed by H. E. S. S. , MAGIC and VERITAS. To ensure the readability of the files and the correctness of the physical units, we plotted each spectrum using GammaPy  [7] , the Science Analysis  Tool chosen by the Cherenkov Telescope Array Observatory, and compared it to the corresponding published figure. In those comparisons, we paid close attention to the EBL-absorption of each spectrum. When the spectra reported by collaborations were deabsorbed for the EBL effect, we included back the absorption using the same EBL model and source redshift as the authors. All the spectra in STeVECat are hence observed spectra.