dataesr/scientific-paragraphs-categorization huggingface dataset:
Our findings on risk factors for major bleeding mostly align with prior investigations  [8, 18]. Our study population was identified and characterized based on relatively coarse criteria as represented in the medical coding in the database. For each one of the acute medical illness conditions (e. g. , infection, cancer, heart failure), there are relevant clinical features such as gastrointestinal infection, cancer type, or heart failure with reduced ejection fraction, that confer a different risk of VTE. In addition, a combination of factors when simultaneously present, such as respiratory insufficiency with infection, can translate to a higher risk of VTE. These issues may further explain the observed heterogeneity in the event rates for VTE and bleeding and the heterogeneity in the duration of enoxaparin prophylaxis.
In summary, more than 90 % of VTE events occurred while not on background prophylaxis. Our study suggests these VTE events are likely enriched for high-risk conditions such as cancer related hospitalization or history of VTE. These conditions represent clinical profiles that are relatively easily identifiable and where consideration of initiation or continuation of VTE prophylaxis appears warranted. Key strengths of our study include a large and contemporary population of interest. Because of the linked nature of the databases, the population was relatively well characterized. We carefully calibrated the algorithm for identification of symptomatic VTE and major bleeding endpoints, utilizing relevant clinical expertise from the coauthors, and enhanced the algorithm well beyond the methodologies commonly employed in studies based on observational data (details available in the Supplement Methods S1 and S2).
Our study also bears key limitations based on analysis of observational data. The study population represented individuals in the USA who were covered under a commercial or Medicare Advantage health plan. Thus, the analysis may not be representative of the non-insured populations, those covered under a public insurance program other than Medicare Advantage (e. g. , Medicaid and other Medicare plans), or international populations. The identification of VTE and major bleeding endpoints relied on an algorithm based on codes and other information available in the database (see Supplement). We cannot rule out the potential for misclassification as compared to the relatively rigorous criteria adopted in RCTs.
Declaration of Competing Interest Dr. Le Gal reports financial support, article publishing charges, statistical analysis, and writing assistance were provided by Sanofi, honoraria for lectures and advisory boards from BMS, Inari, Leo Pharma, Pfizer, Sanofi and Stago. Dr. Agnelli reports financial support, statistical analysis, and writing assistance were provided by Sanofi, honoraria for lecture and advisory board contribution from BMS, Pfizer, Daiichi Sankyo and Anthos Therapeutics; reports a relationship with European Journal of Internal Medicine that includes: board membership. Dr. Darius reports financial support, statistical analysis, and writing assistance were provided by Sanofi. Dr. Kahn reports financial support was provided by Sanofi, and reports research grants from the Canadian Institutes of Health Research and consulting honoraria from Alexion for unrelated educational activities.
Methods and Materials Population This bicentric ambispective study was conducted in the department of radiation oncology [2 sites, situated in the Institut Curie (Paris and St Cloud)] and included all the patients with a locally advanced TNBC who received neoadjuvant chemo-immunotherapy with pembrolizumab and adjuvant RT as part of their treatment between July 2021 and March 2023. The patient registration was prospective for all patients receiving this treatment. This study was approved by the institutional review board of the Institut Curie. Our data collection followed our institutional review board's regulations of the Institut Curie Breast Cancer research and treatment study group with a waiver of informed consent, and the study was conducted according to the Declaration of Helsinki.
Data Collection Three data sets were collected retrospectively from three tertiary referral centers: all patients diagnosed at or referred to (A) the Erasmus University Medical Center, Rotterdam, the Netherlands, between 2002 and 2018 (publicly released  (23) ); (B) Maastricht UMC+, Maastricht, the Netherlands, between 2005 and 2018; and (C) Hôpital Beaujon, Paris, France, included in reverse chronological order starting in 2018, until a total of 201 patients were identified. Imaging data, age, sex, and phenotype were collected for each patient. Inclusion criteria were as follows: HCC, iCCA, HCA, or FNH; pathologically proven phenotype, except "typical" FNH; and availability of a T2-weighted MRI scan. Exclusion criteria were as follows: maximum diameter ≤3 cm; underlying liver disease; or significant imaging artifacts.
Performance of the Radiologists To compare the models to visual assessment, the T2weighted MRI scans were scored by two experienced abdominal radiologists (RAD1 and RAD2). They were blinded to the diagnosis and only had access to the T2weighted MRI but were aware of the inclusion and exclusion criteria of the study. Classification of malignancy was made on a four-point scale to indicate the radiologists' certainty: 1 = benign, certain; 2 = benign, uncertain; 3 = malignant, uncertain; and 4 = malignant, certain. Additionally, the radiologists scored several characteristics used in the decision-making: the presence of (1) central scar (  6 ); (2) liquid; (3) atoll sign  (22) ; and (4) degree of heterogeneity (scale 1-4 similar to malignancy). As the radiologists were from centers A and B, scoring was carried out on data set C to prevent previous exposure to the data.
Data Sets In total, 486 patients were included (A: 187, B: 98, C: 201). The clinical and imaging characteristics are reported in  Table  1. As all centers serve as tertiary referral centers, the data sets originated from 159 different scanners (A: 52, B: 21, and C: 86), resulting in substantial MRI acquisition protocol heterogeneity. Statistically significant differences were found between data sets A, B, and C for magnetic field strength (Kruskal-Wallis: p = 0. 001), manufacturer (p = 10 -4 ), slice thickness (p = 10 -32 ), repetition time (p = 0. 006), flip angle (p = 0. 05), and use of fat saturation (p = 10 -17 ). On the subset that was segmented by two observers, the mean DSC indicated a high average overlap, but the standard deviation also indicated large discrepancies (B: 0. 80 ± 0.
Simply classifying all lesions in patients aged < 49 years as benign, regardless of imaging information, is unacceptable and cannot be applied to the general population. Moreover, the relation in our database may be too strong due to our inclusion and exclusion criteria and thereby not representative of clinical practice. The T2-weighted MRI radiomics model does not use population-based information but predicts the lesion malignancy probability based purely on imaging appearance. Radiomics could be especially useful to avoid missing malignant lesions in young males and to detect benign lesions in older females. Future research should include optimally combining imaging, age, sex, and other clinical factors in the clinical workflow where radiologists typically have access to this information.
ACKNOWLEDGMENTS M. P. A. S. acknowledges funding from the research program  STRaTeGy  (project number  14929-14930 ), which is (partly) financed by the  Netherlands Organisation for Scientific Research (NWO). This study is supported by  EuCanShare and EuCanImage (European Union 's  Horizon 2020 research and innovation program  under grant agreement numbers  825903  and  952103 , respectively). This work was partially carried out on the Dutch national e-infrastructure with the support of  SURF Cooperative.
Opportunities, challenges, strategies and future perspectives To the best of our knowledge, this is the first overview on cancer prevention recommendations in developing codes against cancer in Asia. Our paper includes information, literatures, survey responses and input received from 21 countries in the region on their respective cancer burden, prevention, and control measures. For this overview, we conducted consultations and survey data extraction from ANCCA members who are focal points of respective national cancer centres or health agencies. This collaboration amongst ANCCA members, the rigorous review of evidence, and the development of an ACAC will encourage more public health agencies, policymakers, clinicians, and cancer researchers to continue analysing the evidence to produce more specific cancer prevention recommendations that meet the needs of each region or country in Asia.
Technology serves as an accelerator in the dissemination of information to raise awareness regarding cancer prevention. For instance, the National Cancer Center of China has published an easy-to-understand handbook with cartoon illustrations to raise awareness on primary prevention of cancer. 56  They have also developed the Smart Health Management Digital Platform for Primary Cancer Prevention (SmartHMDP-PCP),  57  to collect data on potential cancer risk factors longitudinally, provide individualised risk prediction and interventional feedback messages, and build databases for cancer research to identify optimal strategies for cancer prevention. In addition to public health education, an ACAC will provide the groundwork for more elaborate cancer prevention tools such as the development of national policies and enactment of certain codes into law.
Population This multicentre retrospective study was conducted in 16 centres in France. The procedures were performed by 18 different senior orthopaedic surgeons. The study included consecutive patients with ICRS stage III or IV osteochondral defects of the knee who were managed by the AMIC ® method with implantation of a Chondro-Gide ® collagen matrix (Geistlich, Root Längenbold, Switzerland) between September 2011 and January 2020 and for whom follow-up was at least 2 years. The patients were identified by searching the Geistlich billing records for patients treated in France. Only patients with defects located on the femoral or patello-femoral part of the knee were included: no tibial lesions were treated. The patient data were obtained by asking the surgeons who performed the procedures to send copies of the patient files.
Assessment methods The following data were collected from the patient files: 36item Short-Form questionnaire (SF-36) score  [12] , Knee Injury and Osteoarthritis Outcome Score (KOOS)  [13] , and International Knee Documentation Committee (IKDC) score  [14]  before surgery and at last follow-up. The Magnetic Resonance Observation of Cartilage Repair Tissue 2. 0 (MOCART 2. 0) score  [15]  was computed for those patients who underwent magnetic resonance imaging (MRI) 2 years after the procedure. All complications were recorded. The SF-36 score was available for 74 patients, the KOOS and IKDC score for 101 patients, and the MOCART 2. 0 score for 58 patients.
The data rates of the transmission demonstration are shown as a function of the transmission distance for 3 wavelength channels across the C-band in Fig. 13a , and shows there is a simple trade-off between total data rate and transmission distance. For comparison between the 15-mode fiber system and a 15-parallel single-mode fiber transmission system, we also measured the throughput of single-mode fiber with a similar length that was placed inside the recirculating loop. An attenuator of 10 dB was placed in the loop to emulate the losses of the mode (de)multiplexer pair. Fig. 13a  shows the decoded throughput measured using this system, multiplied by 15 to indicate the combined throughput of 15 of those systems. The data rate of the 15-mode systems decays faster compared to the single-mode system due to MDL, resulting in a penalty compared to 15 parallel systems.
On-device training should be compulsory for the participant on the first use/setup of their account and remain accessible throughout the study. Rationale: Multiple regulatory guidance and eCOA Consortium expert consensus Regulatory considerations: Regulatory agencies  12, [17] [18] [19] [20] [21] [22] [23]  highlight the importance of training, on-demand access, and accurate documentation, to uphold data quality. The Food and Drug Administration (FDA)  17  indicate that site staff should be well trained on ePRO system functionality, the assessment schedule, and the content of the PROMs in the study. Classification: Class 1.
Rationale: Regulatory recommendations and eCOA Consortium expert consensus. Displaying a default value may bias the respondent's response. Furthermore, default values are not presented on the original, validated paper PROM; therefore, default responses in the electronic implementation may affect comparability. Regulatory considerations: FDA and European Medicines Agency (EMA)  12, 17  discourage pre-population of data fields because they could bias the respondent's response, with some exception for instances when fields are derived from previously entered data. Classification: Class 1.
VI. Provide the Ability to Go Back to the Previous Screen Respondents should be able to navigate back to previous screens to review information and change their responses at any point during ePROM completion and before saving and submitting their data for that data entry session. When navigating back and forth between items, the previous response selection should be presented. Rationale: This best practice is based on eCOA Consortium expert consensus. On paper, respondents can review and change their responses as many times as desired; not enabling this for the electronic implementation could affect measurement comparability. Regulatory considerations: No regulatory guidance available Classification: Class 1.
VII. Capture a Data Point for All Items Unlike paper, items within an ePRO implementation can be programmed to be skippable (users can advance without selecting a response option) or mandatory. In both scenarios, a data point should be saved within the ePRO data set for each item, as either the response option selected or confirmation the respondent chose to intentionally skip the item. If skipping is not enabled for items within a measure, we recommend disabling the "Next" button so that the respondent cannot progress to the next screen until they have selected a response or programming a pop-up message to appear when the respondent selects the "Next" button before selecting a response (Appendix Fig. 3A  in Supplemental Materials found at https://doi. org/10. 1016/j. jval. 2023. 10. 007).
If skipping is enabled for items within a measure, implementing an edit check asking the respondent to confirm that they intend to skip the item (ie, an active skip) ensures it was not accidentally missed. A recommended confirmatory message is Note that skipping does not encompass branching logic (see "Make Items "Self-Contained" When Displaying Them on a Single Screen" section). Rationale: eCOA Consortium expert consensus; evidence is lacking regarding the impact to the data for either scenario. Making responses mandatory for items within a measure is an effort to minimize missing data, which is especially pertinent when the measure supports key trial endpoints. Regulatory bodies  18, 27, 28  cite missing PRO data as a major challenge for the interpretation of treatment efficacy, leading to a low number of PRO-related labeling approvals.
Rationale: eCOA Consortium expert consensus and regulatory acknowledgment of this benefit of electronic data collection, in addition to studies showing the absence of inconsistent responses in electronic versus paper PROMs. [29] [30] [31] [32]  Regulatory considerations: The ability to implement edit and range checks is recognized by regulatory agencies as a benefit to electronic data capture  12, 17  because of the opportunity it provides to improve data unambiguity and data quality by reducing data entry errors. Classification: Class 1.
X. Include a Final Save and Submit Screen Respondents should be informed when they have reached the end of the measure and that they can go back to review or change any responses before saving and submitting them, at which point they will no longer be able to. When navigating "back" through items, respondents' previously selected responses should be displayed (see "Provide the Ability to Go Back to the Previous Screen" section). The eCOA Consortium does not recommend final summary screens that display all items and selected responses. Exceptions to this include when it is appropriate based on the measure setup, such as for an event-driven/episodic diary with multiple entries of the event of interest per day or diaries where responses can be updated. However, here, a summary screen would be presented before the new data entry to remind the respondents of data previously entered to avoid duplication.
Given the possibility of multiple entries per day, a summary screen after completion confirming the data entered thus far could also be implemented. Rationale: eCOA Consortium expert consensus. Not enabling respondents to edit responses once they have submitted their data (without a clear procedure, audit trail, and justification) highlights the importance of reminding respondents they can review or change their responses before submitting. Regulatory considerations: EMA  12  recognizes an advantage of ePROM as minimization of recall bias compared with the risk on paper, given that data can be captured and entered contemporaneously according to time points detailed in the clinical trial design with evidence of such. Data changes should not be permitted at a much later time point without sound justification, and a procedure should be in place to address situations in which a data originator realizes they wish to "correct" recorded data.
XXIII. Create Equal Sized and Spaced Hit Spots for Each Response Option on a Screen Across response types (eg, NRS, VRS) and regardless of response length, each response "hit spot" should be of equal size (ie, even if response options differ in text length, the hit spot should be of equal size), and spacing between response options should be equal to mitigate against bias for certain response options  6  (Appendix Fig. 15  in Supplemental Materials found at https://doi. org/10. 1016/j. jval. 2023. 10. 007). As mentioned, the scope of this work does not extend to complex response scale types (eg, body maps). Rationale: eCOA Consortium expert consensus to ensure data quality Regulatory considerations: No regulatory guidance available Classification: Class 1.
XXIV. Avoid Free-Text Entry Free-text response entry is not recommended when implementing PROMs electronically. Given that items with free-text responses in PROMs are not usually used for scoring, it is acceptable to exclude these items from the electronic implementation. When a copyright holder or sponsor requests implementing free-text entry, the eCOA provider should highlight the risk that the data may be challenging to interpret, code, and manage and should be handled by the sponsor's data management/biostatistics teams. An alternative is providing a prepopulated list of common responses to the item. Rationale: eCOA Consortium expert consensus. Free-text data must be read, deciphered and coded, are rarely included in scoring, and may require translation. Entering free text is also burdensome for respondents and presents the risk that adverse events or other personal data are recorded.
In response to the growing body of evidence, a new ISPOR Task Force was set up to review and update the existing recommendations. 5  The Task Force has outlined an evidence-based approach to determining if measurement comparability testing is needed for a given measure. The suitability of existing evidence should be assessed to ascertain if it sufficiently demonstrates that the change in data collection mode has not affected the PROM's measurement properties. In most instances involving common response scale types, sufficient evidence already exists. The Task Force concludes that, assuming best practices for faithful migration have been followed, further testing of measurement comparability between the data collection modes would be unnecessary. This includes cases of "mixing modes" within clinical trials or with BYOD designs.
1  Usability testing is distinct from user acceptance testing, which ensures a system operates in a manner consistent with the requirement documentation based on the protocol for that study and is typically conducted by the study sponsor or their representative. 41  The most recent ISPOR Task Force report recommends a riskbased approach to whether usability testing is appropriate. 5  This approach should be driven by evidence that participants will not face challenges using the ePRO system. , including usability studies in representative patient groups or unpublished ePRO provider experience. Some instances, such as studies with a specific patient population, with a specific technology, or with a specific study design may require testing to confirm the usability of the data collection mode.
Acknowledgment: The authors thank  Caroline Anfray  ( Mapi Research Trust ),  Piero Bindi  ( IQVIA ),  Bill Byrom  ( Signant Health ),  Stephen Coons  ( C-Path ),  Sonya Eremenco  ( Critical Path Institute [C-Path ]), and  Jamie Hafner  ( Clinical Ink ) for their contributions toward the development of this manuscript. ,collab_author. The eCOA Consortium (formerly the Electronic Patient-Reported Outcome Consortium) was established by C-Path in 2011. With C-Path as its managing member, the other members of the  eCOA Consortium  are firms that provide electronic data collection technologies and services for capturing PRO and other clinical outcome assessment data in clinical trials. The mission of the  eCOA Consortium  is to advance the science of clinical trial endpoint assessment by providing a precompetitive environment in which a critical mass of experts can collaborate to enhance the quality, practicality, and acceptability of electronic capture of clinical trial endpoint data.
Acknowledgements We are grateful to the  Conselho Nacional de Desenvolvimento Científico e Tecnológico -CNPq ,  Coordenação de Aperfeiçoamento de Pessoal de Nível Superior -CAPES , and  Pró-Reitoria de Pesquisa e Inovação -Universidade Federal do Cariri  for the research grant awarded for this study. We thank the  Fundação Cearense de Apoio ao Desenvolvimento Científico e Tecnológico (BPI -FUNCAP :  BP3-0139-00323. 01. 00/18 ; BP4-00172-00223. 01. 01/20 ).
Therefore, when the surveyed area can freely change, Pareto fronts of sampling strategies can be obtained directly through merging RRMSEs previously obtained at distinct a s values, and computing the Pareto front of the pooled dataset. By doing so, we can compare hybrid and fractal sampling strategies and see their respective contributions to a global Pareto front (fig. 5A). Grid design was never a Pareto-optimal design (fig. 5A). It was consistently excluded from the front by other hybrid designs including some degree of randomness and by fractal designs. By contrast pure random designs alone reached a Pareto front very similar if not identical to the Pareto front of all hybrid designs, suggesting that when the size of the area can be adapted it may not be useful to add regular elements within the random design.
Acknowledgements The author thanks  C. Bouget  and  A. Brin  for discussion about the practical implementation of fractal sampling designs,  J. Crabot  for pointing useful bibliography,  B. Laroche  for orienting towards the framework of optimal design, C. Sirami for emphasizing the interest of random designs as a benchmark and A. Tortosa for pointing the need of exploring sampling effort. This research was funded, in whole or in part, by the  Agence Nationale de la Recherche (ANR) , through the  BloBiForM  project grant  ANR-19-CE32-0002-01. A CC-BY public copyright license has been applied by the authors to the present document and will be applied to all subsequent versions up to the.