dataesr/scientific-paragraphs-categorization huggingface dataset:
DISCLOSURE RS is involved in advisory boards for  Amgen, AstraZeneca ,  Bayer ,  BMS ,  Boehringer Ingelheim ,  Janssen ,  Lily ,  Merck ,  Merck Serono ,  Novartis ,  Pfizer ,  Puma ,  Roche ,  Taiho ,  Takeda ,  Yuhan  and has received research grants from  AstraZeneca  and  Boehringer Ingelheim. RK has been an advisor/speaker or received honorarium from  AstraZeneca ,  Pfizer ,  Merck ,  MSD ,  BMS ,  J&J ,  Ipsen ,  Eisai ,  Amgen ,  Novartis ,  Lucence Therapeutics  and  Astellas. NP has consulting or advisory roles with  AstraZeneca ,  Novartis  and  Roche. SP has consulting/advisory roles with  AbbVie, Amgen, Arcus, AstraZeneca ,  Bayer, Beigene, Biocartis, BioInvent, Blueprint Medicines, Boehringer Ingelheim, Bristol-Myers Squibb, Clovis, Daiichi Sankyo, Debiopharm , ecancer,  Eli Lilly, Elsevier, F-Star ,  Fishawack, Foundation Medicine, Genzyme ,  Gilead ,  GSK ,  Illumina ,  Imedex ,  IQVIA ,  Incyte , iTeos,  Janssen ,  Medscape ,  Merck Sharp and Dohme ,  Merck Serono, Merrimack ,  Novartis ,  Novocure ,  Oncology Education ,  Pharma Mar ,  Phosplatin Therapeutics ,  PER ,  Pfizer ,  PRIME, Regeneron, RMEI, Roche/Genentech ,  RTP, Sanofi, Seattle Genetics ,  Takeda  and Vaccibody; has talked in organized public events for  AstraZeneca ,  Boehringer Ingelheim, Bristol-Myers Squibb , ecancer,  Eli Lilly, Foundation Medicine, Illumina ,  Imedex ,  Medscape ,  Merck Sharp and Dohme ,  Mirati ,  Novartis ,  PER ,  Pfizer ,  Prime ,  Roche/Genentech ,  RTP ,  Sanofi  and  Takeda ; has been a principal investigator in trials (institutional financial support for clinical trials) sponsored by  Amgen ,  AstraZeneca ,  Beigene, Bristol-Myers Squibb ,  GSK ,  Merck Sharp and Dohme , and  Roche / Genentech.
Patterns of Neuroplasticity The structural anatomy of the brain is highly variable across healthy individuals, especially at the cortical level  [129] , while variations are less pronounced at the level of the white matter tracts  [18]. Furthermore, advances in non-invasive functional imaging methods which permit the investigation of the functional connectivity have resulted in the development of a large database demonstrating between-subject variability in the distribution of neural networks  [130, 131]. Recent models of neurocognition employed in basic neuroscience have rejected the classical localizationist dogma (one cerebral site underpinning one specific function), and go beyond a simple network organization of the central nervous system (one cerebral circuit underpinning one specific function) by evidencing the critical role of dynamic interplay within and across neural networks which allows behaviors to be constantly adapted to the surrounding world  [132].
Big data is important to new developments in global clinical science that aim to improve the lives of patients. Technological advances have led to the regular use of structured electronic health-care records with the potential to address key deficits in clinical evidence that could improve patient care. The COVID-19 pandemic has shown this potential in big data and related analytics but has also revealed important limitations. Data verification, data validation, data privacy, and a mandate from the public to conduct research are important challenges to effective use of routine health-care data. The European Society of Cardiology and the BigData@Heart consortium have brought together a range of international stakeholders, including representation from patients, clinicians, scientists, regulators, journal editors, and industry members.
16 A flow diagram showing the processes for cleaning and linking different coding sources and datasets can aid understanding of the study design. Reporting of the governance framework underpinning the study from a technical or data stewardship standpoint: • This includes a clear purpose for data gathering and the parameters and time limit of consent, clear mechanisms for data processing ("what happens with my data"), and a description of what the data can and cannot be used for (ie, the mandate given for research). (2) Data security and privacy Working towards a new, sustainable mandate from the public and patients to use their health data may require moving away from abstract rules and regulations and towards more constructive governance, in which trust is a central concept: • The trust of patients and the public in research institutions and in science is pivotal because of the liberties they give to researchers to use their data, which are the product of a social licence based on this trust.
(3) Publications using structured health-care data Accountability for the source of data and how the data have been collected (traceability): • As with data security, a framework of accountability would enable editorial teams in medical journals to be aware of the technical processes before data analysis. Sharing of data, codes, and algorithms used to analyse datasets: • Similar to the requirement for preregistration of clinical trials and prepublication of protocols, journals could restrict publication where the coding within a study is not shared. (Continues on next page) derivation). Preferred standards indicate a high attainment of quality and can be used to improve the future trajectory of research. The checklist (figure  3 ) was created through an iterative process on the basis of the stakeholder proposals and includes five important areas of enhanced transparency: how and why coding was performed; the process of constructing and linking datasets; clear definitions of both diseases and outcomes; the approach to analysis, including any computational methods; and showing good data governance.
The CODE-EHR framework aims to improve the quality of studies using structured health-care data and provide confidence in their use for clinical decision making. A step-by-step approach to completion of the CODE-EHR reporting checklist, with relevant bestpractice examples, is provided (appendix 2). A detailed description of the workflow that led to the checklist is also available (appendix 3). The checklist is available as Word document (appendix 4) and PDF (appendix 5) versions.
Such validation exercises can be preregistered; for example, in the form of a Study-Within-A-Trial. 20  Another possibility is for researchers to provide consented and anonymised gold standard cases to benchmark against, or for data from devices used to verify codes (such as lead fractures). The value of synthetic datasets for validation, which mimic real data, needs further exploration. Mixed-model approaches to collect data on particular endpoints: • May be valuable for situations where the EHR does not reliably collect relevant data; for example, where patients or clinicians, or both, are asked for information, or data are collected via wearable devices or telemonitoring. In some cases, parallel monitoring of patients alongside the EHR study may provide additional confidence (eg, to identify serious unexpected adverse events).
Linkage of different health sources is often a crucial component of research based on structured health-care data, but this aspect is frequently overlooked when reporting such studies. Data privacy and the licence for research can be severely compromised if linkage is not secure. Therefore, our focus is on transparency about how data are coded and linked and how these approaches undertaken by researchers are openly discussed and documented. The stakeholder consensus meetings highlighted clarity of methods as an important concern for future research, supported by evidence that few studies provide sufficient detail to understand the research process. 26, 27 The emergence of registry-based and EHRbased randomised controlled trials (NCT04700826; NCT01093404)  21, 28  reinforces the imperative for improvements to define new concepts for quality research.
Conclusion The CODE-EHR framework was designed by a multistakeholder group to improve the design and reporting of research studies using structured electronic health-care data. Research using these data is a crucial component of future health-care evaluation and administration and will have an increasingly important role in decisions regarding patient care made by regulatory, governmental, and healthcare agencies in every medical specialty. The CODE-EHR checklist asks for clarity on reporting and defines a set of minimum and preferred standards for the processes involved in coding, dataset construction and linkage, disease and outcome definitions, analysis, and research governance. The CODE-EHR framework is expected to enhance research quality and value and to improve research impact using regularly collected health-care data.
Panel: Output from the stakeholder consensus meetings Key consensus statements and advisories (1) Technical process and data stewardship Research using structured health-care data conducted according to the Findable, Accessible, Interoperable, and Reusable [or FAIR] data principles of findability, accessibility, interoperability, and reusability:13 Important challenges: • Various data sources across Effect: • Provide confidence in results • Incorporate EHR-coded studies and within countries • Biased datasets • Different coding systems in regulatory decisions To overcome challenges: • Support new and updated • Identify data sources guideline recommendations • Establish who performed the • Support new EHR studies coding, the coding system used, and the purpose • External validation Important challenges: • Lack of transparency • Uncertain quality • Risk of bias • Representativeness Important challenges: • Variable quality of coding • Multiple algorithms • Lack of transparency • Linkage errors To overcome challenges: • Data assessed for consistency, completeness, and accuracy • External validation of results • Conduct research to identify appropriate uses for EHR • Public engagement Important challenges: • Privacy and consent • Transparency of data sources, code lists, and algorithms To overcome challenges: • Publish code lists • Publish phenotyping algorithms • External validation • Report methods for data preprocessing and linkage To overcome challenges: • Publish completeness of follow-up, handling of missing data, and linkage of datasets • Provide code lists, algorithms, and datasets • Accountability and social licence framework.
3: CODE-EHR framework: best-practice checklist to report on the use of structured electronic health-care records in clinical research Date of completion: Study name: Item Objective F ramework standards Minimum information to provide Lead author acknowledgment 1. Dataset To provide an understanding Minimum: Flow diagram of datasets (a) State the source of any datasets used Choose one from: Directions for use for the construction of how the structured used in the study, and description of (b) Comment on how the observed and (1) Minimum standard not research team: to complete the and linkage health-care data were the processes and directionality of any any missing data were identified and met checklist, authors will need to and coding manuals are ensure that coding protocols the design of the research to consider these points during identified and used linkage performed, published within document prepublished protocol or open access Preferred: Provided within a documents the research report or supplementary addressed, and the proportion observed linkage was performed and the quality of (d) For linked datasets, specify how follow-up (c) Provide data on completeness of for each variable (2) Minimum standard met or (3) Preferred standard met prepublished.
Disease and To fully detail how conditions Minimum: State what codes were (a) Detailed lists of codes used for each Choose one from: file on submission to the outcome and outcome events were used to define diseases, treatments, aspect of the study (1) Minimum standard not journal, with the ability for definitions defined, allowing other conditions, and outcomes prior to (b) Date of publication and access details met readers to review responses. A researchers to identify errors statistical analysis, including those for the coding manual (please add to box (2) Minimum standard met (eg, "this study meets all five of method section is suggested standards in the text of the comment on the meeting of other datasets and repeat the process in Preferred: Provided within a endpoints and components of any composite therapy, procedures, comorbidities, relating to patient identification, (d) Specify any processes used to algorithms used logic and validation of any phenotyping (c) Provide definitions, implementation below) or (3) Preferred standard met the CODE-EHR minimum prepublished protocol or open access validate the coding scheme or reference framework standards for the document prior to statistical analysis to prior work use of structured health-care data in clinical research, with 4.
Analysis To fully detail how outcome Minimum: Describe the process used (a) Provide details on all statistical Choose one from: two out of five standards events were analysed and to analyse study outcomes, including methods used (1) Minimum standard not meeting preferred criteria [add allow independent statistical methods and use of any (b) Provide links to any machine code or met reference to this CODE-EHR assessment of the machine learning or algorithmic algorithms used in the analysis, (2) Minimum standard met the CODE-EHR minimum study meets four out of five of bmj-2021-069048]" or "this paper; https://doi. org/10. 1136/ authenticity of study findings before analyses commencing plan as a supplementary file, locked Preferred: Provide a statistical analysis approaches (d) Specify how generalisability of results any internal validation assumptions, assessing model fit and (c) Specify the processes of testing preferably as open source or (3) Preferred standard met framework standards for the was assessed, the replication of findings use of structured health-care in other datasets, or any external data in clinical research; one of validation the five minimum standards was not met as coding to this CODE-EHR paper; prior to analysis [add reference schemes were not specified 5.
| Study design The study design of InRange (NCT04075513) has been described previously. 24 Briefly, this was a 12-week, phase 4, multicentre (40 sites, seven countries), randomized, active-controlled, parallel-group, openlabel study to compare TIR and glucose variability between Gla-300 and IDeg-100 using blinded CGM. The study was conducted in accordance with the Declaration of Helsinki with all relevant amendments, the Council for International Organizations of Medical Sciences International Ethical Guidelines, the applicable International Conference on Harmonization Good Clinical Practice Guidelines, and all applicable laws and regulations of the countries in which the study was carried out.
2 to ≤ 10 mmol/L), respectively, while avoiding hypoglycaemia. Dose adjustment of mealtime insulin could be based on a pattern of postmeal SMPG data from the prior 3 days (simple titration) or the carbohydrate content of the meal. Baseline CGM values were determined using the blinded CGM device (G6, Dexcom Inc, San Diego, CA) for 20 days at the end of the run-in period after stabilization of current insulins. To be eligible for randomization, a minimum of 10 days (not necessarily consecutive) of useable CGM data generated during the run-in period was required. Eligible participants were randomized 1:1 to receive a once-daily subcutaneous injection of Gla-300 or IDeg-100 in the morning. The randomization scheme was provided by the study statistician to an interactive response technology (IRT) system, which generated the randomization list and allocated participants to treatment arms.
At the screening visit, the investigator contacted the IRT centre to receive the participant number. The randomized treatment period (weeks 0-12) included an insulin titration period (expected to be up to week 8) and a CGM data collection period over 20 consecutive days during weeks 10-12 (to ensure 10 days of evaluable data). Initiation doses of Gla-300 or IDeg-100 corresponded to the previous BI dose. During the titration period, doses of Gla-300 or IDeg-100 were titrated until participants achieved the target fasting SMPG of 70 mg/dl or higher to less than 100 mg/dl (≥ 3. 9 to < 5. 6 mmol/L), while avoiding hypoglycaemia. Dose adjustments for Gla-300 and IDeg-100 were based on median fasting SMPG values from the previous 3 days, including values recorded on the day of titration, as measured by the participants using glucometers.
The mealtime insulin analogue was also actively titrated to achieve the 2-hour postprandial SMPG target (≥ 130 to ≤ 180 mg/dl [≥ 7. 2 to ≤ 10 mmol/L]), while avoiding hypoglycaemia. The dose was titrated at least weekly (but not more often than every 3 days). The best efforts were made to reach the targets by 8 weeks after randomization; thereafter, the dose was maintained until the end of the study. CGM data were blinded to both investigators and participants. Post-treatment safety information was collected 2-4 days after the last insulin dose.
Safety endpoints were assessed in the safety population (all randomized participants who received at least one dose of study drug) and analysed according to the treatment received. These were the incidence and event rates of hypoglycaemia during the on-treatment period reported by each participant and were based on SMPG readings from a glucometer (any, severe [Level ) and the incidence of adverse events (AEs). As a post hoc analysis, the rates of hypoglycaemia were also analysed using CGM data from weeks 10-12 (the number of periods with at least 15 minutes of sensor glucose values of < 70 mg/dl, < 70 mg/dl and ≥ 54 mg/dl, and < 54 mg/dl, respectively, with an event ending when glucose returned to above 70 mg/ dl for at least 15 minutes; an event of < 70 and ≥ 54 mg/dl was defined as a period of < 70 mg/dl without a 15-minute excursion of < 54 mg/dl before the end of the event) and prolonged hypoglycaemia (the number of periods with at least 120 minutes of sensor glucose of < 70 mg/dl).
| Statistical analysis Non-inferiority on the primary endpoint (percentage TIR ≥ 70 to ≤ 180 mg/dl [≥ 3. 9 to ≤ 10 mmol/L]) was tested with a one-sided type I error of 2. 5%, and a relative non-inferiority margin of 10% (note relative, not a difference in % units; e. g. a 10% relative margin for 50% TIR is equal to a 5% difference in TIR). A sample size of 169 randomized participants per treatment group was calculated to provide at least 90% power to show non-inferiority, assuming a TIR of 56% in the IDeg-100 arm, a common SD of 14. 7% and a non-evaluability rate of 22%. The percentage TIR at week 12 (using available data from the 12-week randomized period) was analysed in the intention-to-treat (ITT) population (all randomized participants, irrespective of the treatment received), according to the treatment group allocated by randomization using an analysis of covariance (ANCOVA) model, including the fixed categorical effects of treatment group, randomization stratum of screening HbA1c (< 8.
6] [17]  It is particularly noteworthy that the findings of the non-randomized OneCARE study,  17  which used CGM and TIR (≥ 70 to ≤ 180 mg/dl [≥ 3. 9 to ≤ 10 mmol/L]) as an outcome, and which suggested that the efficacy and safety of Gla-300 and IDeg-100 are comparable in people with T1D insufficiently controlled with first-generation BIs, have now been replicated in this randomized trial. The strengths of the InRange study include its prospective, multi-  F I G U R E 2 CGM-derived and SMPG-derived hypoglycaemia event rate during the on-treatment CGM data collection period (weeks 10-12), for A) Anytime, B) Nocturnal, and C) Diurnal hypoglycaemia. †CGM: number of periods with at least 15 minutes with sensor glucose < 70 mg/dl. Event ends when glucose returns above 70 mg/dl for at least 15 minutes.
the potential to provide a more complete assessment of hypoglycaemia occurrence, particularly nocturnal events, which may be underreported. 31 In InRange, blinded CGM measurement was used and allowed a direct assessment of the properties of the insulins, rather than the effect of CGM. Furthermore, CGM data were blinded for both investigators and participants to avoid treatment decisions being made based on primary endpoint data. However, the InRange study also has limitations. The study precludes conclusions for people with T1D using CGM in standard care, because the study was blinded and TIR was lower than the recommended general clinical target (> 70%). 18 Furthermore, the BIs were administered in the morning and therefore further study would be required to assess evening administration; however, based on the PK/PD profiles of Gla-300 and IDeg-100, a significant impact on the results would not be expected with different dosing times.
ACKNOWLEDGEMENTS The authors thank the study participants, trial staff and investigators for their participation (the list of investigators is provided in the Appendix). The authors thank  Sirisha Pedapudi, MS, MSc  ( Sanofi ), for coordinating the development, facilitating author discussions and critical review of this manuscript. Medical writing support and editorial assistance was provided by  Lois Grant, PhD , of  Fishawack Communications Ltd , part of  Fishawack Health , and was funded by  Sanofi.
Data analysis The data analysis occurred in three steps following a sequential design and through constant comparison. Secondary analysis of the 30 transcripts from the first study. The objective for this first step was to systematically screen the 30 transcripts from the first study again, which had initially been inductively analysed to reveal specific turning points in the lives of the participants. In this secondary analysis, we aimed to examine how the emerging categories of emotion regulation and conflict of loyalty resolution were represented in the data. The turning points revealed in the first study constituted a starting list of categories for the secondary analysis of the transcripts. More precisely, we verified that the turning points were indeed associated with experiences and the expression of emotions as well as conflicts of loyalty, as we had hypothesised at the end of our first study.
Acknowledgments We thank all the women who participated in this study through their narratives, either through their interviews with the first author or through their published life experiences. We would also like to give our special thanks to two 'norm leaders' and experts with lived experience,  Diaryatou Bah  and  Khady Koita  for their valuable inputs at different stages of our study. In addition, we especially thank  Dr Olivier Schmitz  for his valuable input throughout the beginning of this project.
Each of the 8 words lists in the second set included 8 words, one from each different script, resulting in unrelated words. The mean frequency of the words assigned to the "related" vs. "unrelated" conditions did not differ (Fs < 1). In order to illustrate each set with pictures (see Table  3  for an example), we selected all pictures associated with the words  (Deloche and Hannequin, 1997; Bonin et al. , 2003). We changed a small number of words (e. g. , virus -> microbe) to have a better match between nouns and pictures; then we verified that the mean word frequencies still did not differ based on the Manulex database  (Lété et al. , 2004). Second, the three writing prompts and two semantic relatedness conditions (related and unrelated) were organized in 48 slide-shows of 8 slides each: one slide-show for each condition (e.