dataesr/scientific-paragraphs-categorization huggingface dataset:
Furthermore, quantitative assessment of overall treatment satisfaction was performed in 2 out of 12 studies of which one study reported statistically significant results  [78] , whereas one study reported insignificant statistical results  [75]. Out of 12 eligible studies, four studies with relevant numerical data for the primary outcome measure (pain reduction assessment by qualitative measurement with VAS) contributed to this meta-analysis  [75, 79, 82, 84]. Data extracted from a total of 236 patients, evaluated from baseline up to the final follow-up evaluation for each study, were pooled to reveal a statistically significant inter-group difference (MD = -1. 47; 95% CI = -2. 40 to -0. 53; Z = 3. 07 (p = 0. 002), favouring the PBMT group, along with a substantial high heterogeneity (T 2 = 1.
03; X 2 = 18. 85; df = 6; (p = 0. 004); I 2 = 68%) amongst the included studies (Figure  5 ). In total, 5 out of 12 eligible studies with relevant numerical data for the secondary outcome measure (anxiety/depression and QoL assessment by qualitative measurement with OHIP) contributed to this meta-analysis  [74, 75, 79, 82, 84]. Data extracted from a total of 321 patients, evaluated from baseline up to the final follow-up evaluation for each study, were pooled to reveal a statistically significant inter-group difference (MD = -1. 47; 95% CI = -2. 40 to -0. 53; Z = 3. 07 (p = 0. 002), favouring the PBMT group, along with a substantial high heterogeneity (T 2 = 22. 07; X 2 = 119. 45; df = 7; (p < 0. 0001); I 2 = 94%) amongst the included studies (Figure  6 ). A meta-analysis on other outcome variables could not be conducted owing to the disparity in methodology and incomplete or incomparable numerical data.
Material and methods Methodologically, the review used the Patient/problem Intervention Comparison Outcomes (PICO) criteria  [7]. Studies were required to concern adult (≥18 years) LPR patients, diagnosed clinically (symptoms ± signs), and could include complementary examinations (pH monitoring, pH impedance monitoring, pepsin detection) for confirmation. Patients with purely clinical diagnosis were considered as having suspicion of LPR; those with further complementary examination were considered as having definite LPR. Studies were not required to analyze treatment impact, but simply to provide new information on epidemiology, clinical presentation, diagnosis and/or treatment. Pediatric cohorts and case reports were excluded. The study used the PubMed, Cochrane Library and Scopus databases, with search terms: reflux; laryngopharyngeal; laryngitis; gastroesophageal; outcomes; epidemiology; diagnosis; and treatment, over the period 1980-2018.
This study aims to investigate the ways in which an mHealth approach benefits thyroid patients. To do this, we conducted a cross-sectional study among users of the BOOST Thyroid app. First, we used an adapted version of the Short Form 36 Health Survey Questionnaire, a standard for assessing health: physical functioning, role physical, bodily pain, general health, vitality, social functioning, role emotional, and mental health to quantify whether, how and the extent to which information, symptom logging, and analytics features provided in the app were associated with an increase in (1) perceived health, (2) perceived quality of life, and (3) positive interactions between patients and health care professionals. 46, 47 Second, to capture the voices of participants, an open-ended question was included to collect qualitative data on the experiences of using the app for managing thyroid condition.
The survey was distributed to users through the app and was run for 3 months. In addition, we analyzed deidentified data on users' symptoms throughout the first 3 months of the app usage, to get a long-term insight on symptom improvement through app usage. Finally, we compared TSH and body mass index (BMI) levels between the users of the app and the means reported in the literature to avoid introducing bias from differences in TSH and BMI. We found that using the app is helpful to the majority of users. Among users who find the app useful, information came out as the most important feature to improve one's health and quality of life. The study has implications for understanding how mHealth can help manage thyroid conditions.
3. 1. -20. 6. 1. The app underwent a total of two updates, with the addition of logging of thyroidectomy diagnosis and new articles (on COVID-19 pandemics, thyroidectomy, and weight management) to the database of 80 already existing articles. The users of the app are 86. 7% females and 13. 3% males, with age range from 18 to 65+ years (Supplementary Table  S2  and Supplementary Fig. S2A, B ). The study received approval from the Ethics Committee of University of Oxford, SAME_C1A_20_001. The survey was distributed to all users through the BOOST Thyroid app home screen (the first screen users come to when opening the app, Supplementary Fig. S1A ). Participants had used the app between 7 days and 2 years before taking part in the survey. Survey responses were collected from March 2020 to July 2020, a period set a priori.
Data Participants were first asked whether the mHealth solution has helped them ''in general. '' Only participants who indicated that the app is useful continued to participate in the survey to draw upon the positive experiences of mHealth solutions for underactive thyroid. To assess how the app is helpful to users, we included binary, single-choice, and multiple-choice questions (Q1-8, Supplementary Table  S1 ). To capture participants' experiences in context, the survey also included the free-text question ''Can you tell us 3 ways the BOOST Thyroid app has influenced your health?'' To evaluate users' quality of life before and after using the app, we used a modified version of the Short Form 36 Health Survey Questionnaire. 46, 47 We included specific and appropriate questions relating to the health and the quality of life of individuals with an underactive thyroid, reflecting the most common symptoms and the most common day-to-day life problems based on the scientific literature (Refs.
21, 22, 44, 48 nd Supplementary Table  S1 ). Some questions are measured on a 5-point Likert scale; others are measured on a binary scale or allow for multiple answers (Supplementary Table  S1 ). The resulting questionnaire resulted in a maximum of 19 questions, asked depending on users' answers. Finally, data on symptoms, age, BMI, and TSH values previously entered in the app by users were paired with survey data. In addition, we analyzed anonymized data of five (  5 ) most tracked symptoms from 115 users at day 0 (first day of the app usage) and day 90 of app usage. These five symptoms correspond to additional questions present in ThyProÔ questionnaire, specific for all the thyroid conditions (Ref. 49 and Supplementary Fig. S3 ).
Statistical analysis Raw data were analyzed and drawn using PrismGraph software. Categorical variables were summarized as counts and percentage, whereas numerical variables were summarized as mean and SD (or median and IQR). We first compared participants with the larger pool of anonymized app users with regard to age, BMI, and TSH levels. Second, to compare participants' self-reported quality of life before and after using the app, two-sided chi square tests for comparing proportions of categorical variables and nominal symmetry tests for paired data were performed using the R software and the package ''rcompanion. ''  50, 51 alysis of qualitative data The qualitative data were analyzed using NVivo12. 52 articipants' quotes were reviewed and tagged with ''codes'' to extract themes from the data independently by two members of the research team trained in qualitative analysis.
61 Yet there remain some barriers to applying mHealth more fully, including doctor's resistance,  62, 63  high data entry burden for patients, and potential loss of interest in the long term. 64 The lack of access to affordable internet in some localities might cause reduced availability of mHealth tools and free health information. 65, 66 Furthermore, some might argue that the quality of the information when designing and building mHealth solutions is not always appropriate, and scientifically accurate. However, the data protection regulations and laws spanning the EU, United States, Canada, and Brazil give a lot of power back to users of these technologies in terms of data ownership, including the opportunity to decide whether one wants to remove their data from databases and the rights to know about the process and purpose of digital tools.
Data collection Between October 2018 and March 2019, 24 in-depth interviews and two focus groups (with 5 and 7 participants in each group) were conducted with adolescent girls (aged 15-16) and 21 indepth interviews were conducted with their mothers. Participants were purposively selected to include both vaccinated and unvaccinated girls from different districts in Paris. However, only eight vaccinated girls were identified for this study, three of whom took part in focus group discussions. Participants were identified using two recruitment methods. Adolescents were first approached through public, private and professional Lycées (high schools), with prior approval from the school directors. However, as only four interviews and two focus groups were conducted using this recruitment method, a local research agency specialised in behavioural research (BVA Group) was contracted to identify additional participants.
Data was analysed using a coding framework, developed by deductively drawing parent codes from the topic guides, existing literature and contextual background information and the analytical memos. The coding framework was tested on four transcripts and finalised using an inductive process to derive additional codes. The final framework used to code remaining transcripts was reviewed by a second researcher, allowing some codes to be modified, merged or removed during the analysis. Transcripts from interviews with mothers and adolescent girls and focus groups with adolescent girls were coded separately to detect differences and similarities in the coding phase. Coded excerpts as well as the language used by participants was then compared between mothers and adolescent girls, with particular attention paid to exchanges between adolescent girls during the focus group to understand how HPV vaccination is discussed among peers.
Study limitations This study provides a valuable analysis of the role of maturity in adolescent decision-making around HPV vaccination. However, some results have to be interpreted with caution. As this was an exploratory study, maturity was not measured against any tools and findings focused on thematic analysis instead. Further research could explore associations between maturity levels, assessed through such scales, and willingness to accept HPV vaccines. All adolescent girls included in this study were from Paris, and most of them were not vaccinated against HPV, which could have skewed some of the findings. Data was collected with 15 to 16 years old girls, to obtain a sample of girls who had passed the age of being offered the vaccine. While our findings around maturity may not apply to younger girls, they provide important information around the possible involvement of older girls in decisionmaking.
The use of two different recruitment methodologies may also have influenced the final sample of participants included in the study. Girls who accepted to take part in the study may have been more mature than those who refused, influencing some of the findings. Findings from this study are only applicable to adolescent girls: no interviews were conducted with adolescent boys as they were included in the HPV vaccination programme in France in 2020, after data for this study was collected. Future research could explore the differences in the role of maturity among boys and girls in HPV vaccination decision-making.
Build and use models Models to help in participatory planning require data. There is a need to find and ensure access to datasets at multiple spatial scales (6a). In order to debate the potential effects of city actions, the indicators upon which judgments will be based need to be agreed (6b). These will include environmental indicators such as carbon emissions and particulate matter concentrations, health outcomes such as cardiovascular and pulmonary disease burdens, and intermediate indicators such as transport modal share and electricity supply. A number of models have been developed (including CRAFT, microsimulation, System Dynamics, and air pollution) to compare the potential effects of actions on environmental and health outcomes. These need to be assessed for relevance to city context and data availability and may need to be adapted to examine new exposures and outcomes, particularly those related to health (6c).
Synthesise global evidence 5 Assess if objectives can be met A sufficient extent of relevant global evidence has been reported in the academic literature It is possible to extract transferable knowledge from disparate global evidence. Stakeholders find global evidence intelligible and relevant to their situations. 6 Build and use models Relevant datasets are available. Agreement is reached on indicators for each city. Models and microsimulation can generate useful data at city level. 7 Compare scenarios Decision-makers are interested in comparing scenarios and find the comparisons credible. Plans can vary on the basis of model predictions and simulations are given weight in decision situations with multiple competing agendas. 8 Plan together Meaningful interaction between decision-makers, researchers, and citizens is possible.
This work was supported by the  Wellcome Trust  through the  Our Planet, Our Health  award, ' Complex Urban Systems for Sustainability and Health (London Hub) ' [209387, https://doi. org/10. 35802/209387]and a Senior Clinical Research Fellowship [206417, https://doi. org/10. 35802/206417] to DO. KZ is funded by a  National Institute for Health Research (NIHR) School for Public Health Research (SPHR)  PhD Studentship [PD-SPH-2015]. The views expressed are those of the author(s) and not necessarily those of the  NIHR  or the  Department of Health and Social Care. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
Procedure Data were collected anonymously at four timepoints between 23 April and 11 December 2020, via an online survey designed with Qualtrics software (Qualtrics, Provo, UT, USA). To track changes in the students' psychological state, we conducted measurements at four timepoints: (1) during France's first national lockdown (between 23 April and 8 May); (2) during the period after lockdown when universities remained closed and the summer vacation had begun (9-23 June);  (3)  when universities were open (12-23 October); and (4) during the second lockdown (between 20 November and 11 December). For the first time (Time 1), a link to the survey was sent by e-mail to teachers in several faculties (i. e. , Science, Psychology), at various French universities (i. e. , Nîmes, Lorraine), and was also distributed via students' social media (e.
g. , Facebook groups). Our only criterion for inclusion was to be a student at a French university. Participants agreed to participate in this study after reading a consent form. They were informed that their participation was voluntary, and they could withdraw at any time. No personal data allowing the identification of the participants were collected, except their email addresses. These data were separated from the other data and exclusively used to send invitations to participants at each measurement time. All the procedures contributing to this work were undertaken in compliance with the ethical standards of the relevant national and institutional committees on human experimentation and with the 1975 Declaration of Helsinki, revised in 2008.
Intervention. Participants were asked to walk in the laboratory at self-selected speed, in a straight path. The control group was recorded once. Conversely, the PD group was acquired twice: during the first acquisition the patients were in off state (no L-DOPA intake in the last 14-16 h) (PDoff group), while in the second acquisition the subjects were recorded 40 min after taking a subclinical dose (defined as half of their usual morning intake) of L-DOPA (Melevodopa + Carbidopa) (PDon group). Before each acquisition, the PD subjects were tested through UPDRS-III. For each participant and each condition (off and on in PD group), four gait cycles were acquired and averaged for data analysis. Acquisition system. The gait analysis took place in the Motion Analysis Laboratory of the University of Naples Parthenope.
Trunk displacement index. Beyond typical gait parameters, according to our initial hypothesis, we designed a new measurement method to evaluate the trunk displacement. Firstly, we calculated the 3D trajectory of the centre of mass COMt of each subject and its mean COMt during gait. Then we looked for an anatomi- cal reference point, representative of the trunk position and located it between acromia. As for the COM, we calculated the trunk trajectory Tt. Then, we calculated the distances of each point of COMt and Tt from COMt , respectively named COMd and Td , during the whole gait cycle as (Fig. 2a ): obtaining their respective vectors of distances in all three planes. To enclose the three-dimensional data in a unique value, for each vector of distances we calculated its norm and summed the results separately.
Results Demographic, clinical, neuropsychological evaluation. In the present study, we evaluated 23 PD patients (in on and off state) and 23 healthy controls. As shown in Table  1 , no statistically significant differences have been found for demographic, anthropometric and neuropsychological data except for the UPDRS-III in the PD, between off and on condition, with the PDoff group showing higher UPDRS-III values. (1) COMd = COMt -COMt (2) Td = Tt -COMt (3) TDI = �Td� �COMd�.
Setting and participants The climatic health resort of Balaruc-les-Bains is the most frequently visited spa in France, welcoming over 46,000 spa users per year, essentially subjects with osteoarthritis pains  [13]. Between May 1st and September 30th 2017, all subjects aged 65 and over visiting the Balaruc-les-Bains centre were invited to participate in the study. Eligible subjects were those able to comply with all trial procedures, i. e. able to fill in questionnaires and with no contraindication to performing physical measurements of gait, balance, and muscle strength. The study was approved by the Clinical Research Ethics Committee of the University Hospital of Nîmes (France) (IRB-CHU-Nîmes16. 04. 05; ClinicalTrials. gov ID: NCT04277000).
The eligibility criteria aimed to exclude factors substantially affecting usual gait or the quality of 3-dimensional (3D) data. We thus included a cohort without obvious predisposing factors to gait/balance impairments. For example, peripheral neuropathy and obesity may cause functional gait and balance impairments and have been associated with falls in PWH  [12] [13] [14] [15]. Increased awareness of gait deviations within a population without obvious risk factors could contribute to more regular screening, which may be useful for reducing walking-related risks or injury severity. Furthermore, subcutaneous tissue movement is known to influence 3D data collected using body-worn inertial motion capture systems; this is less likely with lower BMI ranges  [16]. Human immunodeficiency virus status was confirmed via rapid blood test.
Study Procedures Data were collected at the 2 sites in quiet, hard-floored rooms. The study visit included questionnaire completion, physical measurements, 3DGA, and random-sequence functional test performance. To standardize the testing protocol and facilitate skin-attachment of IMUs, participants were barefoot and dressed comfortably. Practice trials for 3DGA were performed after the IMUs were positioned, but before IMC calibration. For the 3DGA trials, conducted using standardized procedures, participants walked along a 10-meter walkway under 3 randomized conditions: (1) self-selected usual pace; (2) as fast as possible without running; and (3) with a DT. Participants performed 3 trials per condition. Walking started 1 meter before a line on the floor and ended after crossing a second line.
Data Management Inertial measurement unit data were transmitted wirelessly to a recording laptop and exported to MATLAB software (R2017a; MathWorks) for processing. Three successful 3DGA gait trials per condition were analyzed per participant. Cyclical gait events (foot contact and foot-off) were determine using a built-in algorithm in the software. This segmented the data into gait cycles normalized in time to 101 data points at 1%-time intervals. After gap-filtering, outcomes were (1) determined using analysis scripts and visualizations and (2) exported to MS Excel for analysis. The EGVI calculations were based on 5 TSPs generated within the 3DGA software, custom-formatted for use within the Excel EGVI macro  [25]. Raw data were obtained from the 3 gait trials per participant. Within each trial, EGVI calculation was conducted if at least 3 consecutive values for each alternative parameter were available  [36].
Clinical tests and 3DGA data were compared between groups using factorial ANCOVA. For clinical tests, covariables were age, sex, leg length, anxiodepressive symptoms, smoking status, and physical activity. For 3DGA, covariables included dimensionless (gravity-and leg-length-normalized) gait speed, age, and sex. Separate ANCOVA models were created for each dependent variable, treating clinical tests as continuous variables in all analyses  [28]. Where significant interactions were noted between HIV-serostatus and the factors/ covariables included in the ANCOVA, an "HIV × covariable" term was included in the final model  [38]. Nonsignificant interaction terms were serially removed from the model  [38]. Correlations between clinical tests and EGVI scores, selfreported function, and fall history were evaluated using Spearman's rank (r s ) correlation coefficients and Pearson product-moment (r) coefficients.
RESULTS Of 186 participants screened, 106 were eligible, and after further exclusion, 50 PWH and 50 SNPs participated in all tests (Supplementary Figure  1 ). Dual-tasking data were collected for 41 of 50 PWH and 47 of 50 SNP. Due to insufficient detection of consecutive strides, usual-pace EGVI scores were calculated for 44 PWH and 29 SNP, DT EGVI for 26 PWH and 30 SNP, and fast EGVI for 0 participants.
Acknowledgments We thank study participants for their time and effort and the  Cape Winelands HAART  to HEART team, research nurses, and human immunodeficiency virus counselors for assisting with data collection and study logistics. In addition, we thank  K. Grimmer  for providing writing support for this manuscript. We gratefully acknowledge the relevant  Community Health Clinics and Stellenbosch University Ukwanda Centre for Rural Health  for hosting data collection. Author contributions. K. B. and Q. L. contributed to conceptualization; K. B. , Q. L. , and A. G. contributed to methodology; K. B. contributed to formal analysis; K. B. , Q. L. , H. S. , M. F. E. , and I. W. contributed to investigation; Q. L. , H. S. , M. F. E. , I. W. , and K. B. contributed to resources; K. B. contributed to writing the original draft; K.