dataesr/scientific-paragraphs-categorization huggingface dataset:
25 0. 74 Dataset CA RF Nb rules Cov Int CA GB MSE Nb rules Cov Int MSE Diabetes 7. 6 0. 99 17. 95 0. 70 9. 1 0. 99 19. 7 0. 74 Prostate 5. 8 1. 0 13. 95 0. 85 6. 05 1. 0 13. 35 0. 86 Ozone 3. 3 0. 99 5. 85 0. 42 3. 95 0. 99 6. 55 0. 39 Machine 2. 85 0. 99 5. 05 0. 40 3. 25 0. 99 6. 05 0. 50 MPG 3. 1 0. 99 4. 3 0. 28 3. 65 0. 99 6. 85 0. 30 Boston 4. 9 0. 99 10. 9 0. 45 6. 8 0. 99 15. 15 0. 45 Student 18. 45 0. 99 51. 4 0. 86 32 0. 98 86. 75 1. 01 Dataset CA SGB Nb rules Cov Int RuleFit MSE Nb rules Cov Int MSE Diabetes 8. 75 0. 99 19. 8 0. 67 249. 35 1. 0 1168. 7 0. 71 Prostate 6. 5 1. 0 15. 05 0. 76 78. 4 1. 0 268. 8 0. 66 Ozone 4. 05 0. 99 6. 5 0. 38 187. 45 1. 0 851 0. 28 Machine 3. 2 0. 99 6. 1 0. 52 99. 85 1. 0 278. 15 0. 08 MPG 3. 75 0. 99 7. 25 0. 29 174. 7 1. 0 745. 15 0. 13 Boston 6.
35 0. 99 13. 85 0. 46 264. 95 1. 0 1207. 65 0. 12 Student 37. 3 0. 98 100. 05 1. 01 277. 95 1. 0 1280. 4 0. 90 Dataset NodeHarvest Nb rules Cov Int SIRUS MSE Nb rules Cov Int MSE Diabetes 227. 15 1. 0 613. 05 0. 59 21. 25 0. 99 32. 35 0. 57 Prostate 66 1. 0 160. 35 0. 57 23. 25 0. 99 33. 7 0. 59 Ozone 181. 45 1. 0 472. 9 0. 29 24. 9 1. 0 39. 4 0. 32 Machine 67. 15 1. 0 159. 0 0. 32 13. 9 1. 0 21. 85 0. 26 MPG 100. 15 1. 0 257. 85 0. 15 25. 0 1. 0 45. 2 0. 21 Boston 139. 85 1. 0 345. 4 0. 20 25. 0 0. 99 38. 5 0. 25 Student 78. 5 1. 0 228. 45 0. 76 15. 35 1. 0 26. 2 0. 77.
Time-window A predefined time frame before and after the protocolspecified PRO assessment timepoint, whereby the result would still be deemed to be clinically relevant. 23 *The terms outcome and endpoint are often used interchangeably, although this is not always consistent with the range of definitions available. For the definitions included in this glossary, an endpoint is defined from PRO data (ie, the outcome) by fully specifying four components: measurement variable (eg, fatigue 'in the past week' as measured by the QLQ-C30), analysis metric (eg, change in fatigue from baseline, final fatigue value, time to clinically important increase in fatigue (and 'event'), method of aggregation (eg, median fatigue, proportion of patients with severe fatigue, proportion of patients with clinically important change in fatigue) and timepoint.
Modelled after other reporting guidelines,  2 24 25  this E&E paper presents each checklist item with at least one example from a trial protocol, followed by an explanation of the rationale and main issues to address, to facilitate understanding and usage. The guidelines are intended to be used in conjunction with the SPIRIT-PRO Extension, SPIRIT 2013 Statement and E&E paper and other relevant extensions. 1 2 10 26  Empirical data and references to support each SPIRIT-PRO item are provided. Real-world examples for each SPIRIT-PRO item, quoted verbatim, are presented to reflect how key elements could be appropriately described in a trial protocol. These examples were obtained from E&E paper authors, public websites, journals, trial investigators and industry sponsors. Some examples illustrate a specific component of a checklist item, while others encompass all key recommendations for an item.
Open access (during trial start-up and conduct). Acknowledgements of PRO protocol input from patient-partners as per guidelines for the reporting of patient and public involvement is also recommended. 28 Patient and public involvement in all aspects of trial design, including but not limited to: selection of outcomes and measures, timepoints, mode of assessment and reporting, can help minimise burden and ensure that data collected is patient-centred and relevant to participants and to the future patients who will benefit from the research. Only 7 of 75 (9%) protocols that included PROs from the UK National Institute for Health Research (NIHR) Health Technology Assessment programme explicitly described who was responsible for the PRO component (online supplemental 1). 3.
Example The Symptom and Personal Information Questionnaire #1, the Cancer Rehabilitation Evaluation System Short Form and Cover Sheet must be completed prior to registration and randomisation on SWOG-9133. 39 planation Any eligibility criteria relevant to PRO assessment should be considered during the trial design and clearly specified in the protocol for consistent use by research personnel. In some trials, the baseline PRO assessment is required before randomisation as an eligibility criterion. 30 This helps to ensure there will be a valid baseline questionnaire from all patients, which is essential for calculation of change scores, or inclusion as a covariate in modelling longitudinal PRO data. For unblinded trials, this also ensures PRO data are collected before participants are aware of the randomisation which may affect some aspects of the participant's response, for example, anxiety/emotional well-being.
Explanation For each outcome, including PROs, the trial protocol should define four components: the specific measurement variable, which corresponds to the data collected directly from trial participants (eg, Beck Depression Inventory score, all-cause mortality); the participant-level analysis metric, which corresponds to the format of the outcome data that will be used from each trial participant for analysis (eg, change from baseline, final value, time to event); the method of aggregation, which refers to the summary measure format for each study group (eg, mean, proportion with score >2) and the specific measurement timepoint of interest for analysis. 1 Many PRO questionnaires are multidimensional, assessing multiple facets of the impact of a disease and its treatment and usually include multiple assessments over the course of the trial.
Open access PRO assessment require an unambiguous reference point, to ensure that PRO data collection captures clinically relevant timepoints of interest. In deciding the size of the time-window for a PRO assessment, consider the trade-off between a smaller, more precise, time-window and a larger more feasible window. One approach is to specify a time-window that is a little larger than the ideal and not allow exceptions; this approach is more consistent than setting a smaller time-window and allowing exceptions. Often PRO assessments that occur during active treatment, for example, chemotherapy, have a smaller time-window to capture acute toxicity that arise and resolve relatively quickly, while those occurring many months or years after treatment completion can have a larger window if the participant's outcomes are expected to stabilise over time.
PRO endpoints: 2°T he sample size for this trial, based on the primary endpoint (time to local recurrence of invasive or intraductal breast cancer in the ipsilateral breast), was 1600 patients. The sample size for the PRO substudy was determined a priori, and was less than that required for the primary endpoint, as explained in the protocol excerpt below. Therefore, patients recruited after the PROspecific target sample size was achieved did not complete PRO questionnaires, saving trial resources in data collection and management. 'Sample size determination: for the quality of life study aiming to detect a difference between the tumour bed boost and no boost groups of 0. 2 SD of a continuous scale such as fatigue or physical symptoms, with 80% power at a two-sided alpha level of 5%, the required sample size is 790 patients.
METHODS: DATA COLLECTION, MANAGEMENT AND ANALYSIS SPIRIT-18a(i)-PRO Extension Justify the PRO instrument to be used and describe domains, number of items, recall period, instrument scaling and scoring (eg, range and direction of scores indicating a good or poor outcome). Evidence of PRO instrument measurement properties, interpretation guidelines and patient acceptability and burden should be provided or cited if available, ideally in the population of interest. State whether the measure will be used in accordance with any user manual and specify and justify deviations if planned.
67 68 planation Standardisation of all aspects of PRO administration is vital to PRO data quality. It is therefore critical that research personnel and trial participants understand how, when and where PRO data will be collected in the study. 10 The study protocol should specify the permitted mode(s), method(s) and setting(s) of PRO data collection, including the permitted 'back-up' options and preplanned reminders. For example, when PRO assessment is conducted in clinic via a tablet computer, paper forms could be permitted (and available) as a back-up option for instances when the tablet is not available or functioning properly. Offering alternative modes of completion may help improve response rates. 30 Of note, the FDA has previously recommended that there is a back-up plan for electronic PRO data collection (eg, webbased, phone-based or paper-based) implemented in case of malfunctions with electronic devices.
70 71 The International Society for Pharmacoeconomics and Outcomes Research provide useful guidance on key considerations for PRO data collection including multiple modes. 71 72 he setting for PRO data collection, for example, in clinic or at home (or clinic at baseline, with follow-up at home), should be described and standardised across trial intervention groups and sites. Differential use of settings and modes of administration by treatment arm should be avoided as these may lead to different response rates and potentially biased results. 11 he protocol should also specify the types of assistance trial staff can provide patients for completing the PRO assessment. Respondents should be encouraged to selfcomplete as far as possible. Some respondents may require some assistance, however, the greater the degree of assistance, the greater the potential to influence a respondent's responses.
Using different language versions of PRO instruments to collect data in a trial ideally requires evidence to support the psychometric equivalence of data being reported, especially if data are going to be pooled for clinical trial evaluation. 52 82 83 Where such evidence is unavailable, prespecification in the statistical analysis plan (SAP) of exploratory analyses to assess whether there are differences between PROs by language group may be appropriate. The language in which each patient complete the questionnaire should be recorded in the database to inform such analyses. 52.
32-0. 34). The questionnaire has been applied to several clinical groups in Norway. '  84  Trial name: evaluating the effectiveness and cost effectiveness of dementia care mapping (DCM) to enable person-centred care for people with dementia and their carers: a cluster randomised controlled trial in care homes (DCM EPIC study 1. 0) PRO endpoints: 2°' Relative/friend criteria To be eligible to provide proxy data about a resident, relatives/friends must: have visited the resident on a regular basis over the past month (ie, at least once per week). Be willing to provide data at a time convenient to them. Have sufficient proficiency in English to contribute to the data collection required for the research. '  85.
Just as the measurement properties of the PRO instrument should be specified, so should the properties of measures to be used by proxy reporters. Given known issues with patient and proxy reporter discordance, 88-91 while patient-participants are still able to self-complete, collecting both participant and proxyreported data enables quantification of the size and direction of any bias, that may later be adjusted for, if needed. Further data be gathered about the proxy (eg, age, relationship the patient, gender, proxy relationship and exposure to the patient  92  as these variables may guide interpretation of results and any subgroup/ sensitivity analyses. Whether proxy-reported data will be analysed separately or pooled with participant-reported data should also be detailed. Any such plans should be specified in the protocol and SAP.
30 From a regulatory perspective, the FDA encourages maintaining in assessment methods, however, this should balanced with reduction of missing data. If different modes are used, they should be justified and presented in the study documentation. 99 If different modes are used, 'FDA will review the comparability of data obtained when using multiple data collection methods or administration modes within a single clinical trial to determine whether the treatment effect varies by method or mode. ' 69 Additional strategies are described in the full review. 30 he MITO-2 and MA. 17R examples above illustrate some of these strategies. MA. 17R comes from the Canadian Cancer Trials Group (CCTG), a multicentre cooperative oncology group that conducts clinical trials in cancer therapy, supportive care and prevention.
The former problem will be minimised by ensuring that participating centres are properly informed and motivated towards HRQL assessment. During the study, compliance with completing HRQL questionnaires will be investigated at each timepoint. The compliance of the HRQL assessments will also be reviewed twice a year and will be a part of the descriptive report by the Data Center for the Group's plenary sessions and, if possible, be presented by the EORTC Quality of Life Group's appointed liaison person. The compliance rate between the two arms will be compared at each timepoint using a χ 2 test. In order to adjust for the multiplicity of the tests, a Bonferroni adjustment will be made by which each test will be performed at the 0. 01 significance level. Should follow-up compliance levels drop below 60% at subsequent biannual compliance reviews, then the Protocol Writing Committee would review this to either improve compliance or consider terminating the HRQL assessment in the trial.
3  July 12, 2021 by guest. Protected by copyright. http://bmjopen. bmj. com/ BMJ Open: first published as 10. 1136/bmjopen-2020-045105 on 30 June 2021. Downloaded from access There two types of missing PRO data: (1) missing when participant completes but not all of the questions within a instrument and (2) missing assessments, when the participant does not complete a scheduled assessment at all (ie, there are no PRO data available for analysis from the participant at that assessment timepoint). The latter type is more serious, as it potentially affects the choice of analysis method and the interpretation and generalisability of the results. The trial protocol should explain how both types of missing data will be handled in the analysis. 1. Handling missing items: many PRO scoring manuals provide guidance for handling missing items.
Any arising interventions should be recorded. Evidence suggests the absence of such information leads to inconsistent handling of concerning data including administration of non-prespecified interventions to aid the trial participant which risks co-intervention bias that is, bias caused by 'any intervention other than the experimental manoeuver that alters the frequency of a trial's outcome of interest'. 12 116 117 Identifying participants at risk and in need of urgent attention through PRO monitoring is an ethical issue as is acknowledging that information identified in the completion of a PRO too may be shared with the clinical team when the need arises. Information about how the trial staff will respond to concerns, or alternative support mechanisms where monitoring is not taking place, should be provided to participants (in participant information and consent documentation).
The ultimate measure of the performance of any health service must be in whether or not it helps people recover from an acute illness, live well with a chronic condition and face the end of life with dignity-and people's own reports on their own condition are the only valid way to gauge success. So if a drug or treatment is to be trialled for use in healthcare delivery, it is essential that PROs are included in the success criteria. It is equally essential therefore that participants on the study understand the importance of completing PRO assessments and to understand how and why the data are collected. This should not be too onerous for researchers to explain to patients. Patients who choose to participate in clinical trials do so because they wish to benefit others and if possible, to benefit themselves.
DISCUSSION This paper provides a detailed rationale, implementation instructions and real-world examples to assist investigators to develop the PRO-specific components of clinical trial protocols, in accordance with the 16 items of the SPIRIT-PRO Extension. This SPIRIT-PRO Extension E&E paper is recommended for use alongside the original SPIRIT 2013 and SPIRIT-PRO guidelines. 1 2 10  The mission of the SPIRIT-PRO Group is to improve the design and standardisation of PRO components of clinical trials and thereby ensure high-quality PRO data to inform patientcentred care. To further facilitate uptake of the SPIR-IT-PRO items, we have provided a PRO-specific protocol template covering all the SPIRIT-PRO items. This can be used in two ways: either incorporated item-by-item into relevant sections of existing clinical trial protocols or retained whole as a dedicated PRO section within a trial protocol.
The use of a template should support investigators to address all required SPIRIT-PRO checklist items comprehensively and meaningfully, in conjunction with the real-world examples provided for each SPIRIT-PRO item in this manuscript. The overall aim of the SPIRIT-PRO Extension and E&E is to improve the completeness, quality and transparency of PRO sections of clinical trial protocols, where PROs are a primary or key secondary outcome. We also recommend use of the guidelines to support development of protocols where PROs data are exploratory in nature, including single-arm trials with PRO endpoints. Many of the SPIRIT-PRO items may also provide useful prompts about PRO content for cohort studies and other nonrandomised designs. The SPIRIT-PRO guidelines,  10  and E&E paper aim to facilitate development of high-quality PRO protocol content, which will ultimately also facilitate the review of protocols by research ethics committees/ IRBs, scientific review groups and funders.
Improved PRO protocol content has been associated with more complete reporting which will help facilitate the critical appraisal of final trial reports and results  3  and use of PRO data to inform patient-centred care. Several SPIRIT-PRO items correspond to items on the CONSORT-PRO checklist. 10 19 This is particularly important since reviews of PRO reporting indicate that, where published PRO trial data were available, there was often considerable delay between publication of primary outcomes and the PRO results and standards of reporting were poor. 4 5 7 9 120-123 orryingly, a recent review of cancer trials suggested that 49 568 participants were involved in studies that failed to publish their PRO data and that poor reporting was associated with suboptimal PRO protocol content. 9 This finding is consistent with findings from Schandelmaier et al, which demonstrated that 52% of cancer trials specified HRQL outcomes in their protocols, however, only 20% reported any HRQL data in associated publications.
Acknowledgements The authors would like to thank  Trish Groves, MRCPsych. Kluetz P, MD ,  US Food and Drug Administration ,  Jeanette Kusel ,  MSc ,  National Institute for Health and Care Excellence ,  Laura-Lee Johnson, PhD ,  US Food and Drug Administration ,  Joanna Coast, PhD ,  University of Bristol  and  Doug Altman, DSc  for their contribution to the  SPIRIT-PRO. The  SPIRIT-PRO Group  gratefully acknowledge the additional contributions as detailed in reference [10], Appendix (refer to online supplemental file 2) made by the  SPIRIT-PRO Executive , the  ISOQOL Best Practices for PROs in Randomised Clinical Trials Protocol Checklist Taskforce , the international stakeholders responsible for stakeholder survey distribution and stakeholders who completed the stakeholder survey, the Delphi panellists and the  SPIRIT-PRO International Consensus Meeting Participants.
Obtained funding: MC, DK, MK,  Doug Altman , JBl, JBr, MB,  Joanna Coast, HD, MvH ,  J Ives , RM-B, GP,  L Roberts , AS. Supervision: MC. Funding This SPIRIT-PRO Extension was funded by  Macmillan Cancer Support  (grant  5592105 ) and the  University of Birmingham  and was sponsored by the  University of Birmingham. Development of the PRO protocol template was funded by an unrestricted educational research grant from  UCB Pharma. MC receives funding from the  National Institute for Health Research (NIHR) Birmingham Biomedical Research Centre , the  NIHR Surgical Reconstruction  and Microbiology.
6b Explanation for choice of comparators Objectives 7 Specific objectives or hypotheses SPIRIT7-PRO State specific PRO objectives or 12-14 Extension hypotheses (including relevant PRO concepts/domains). Trial design 8 Description of trial design, including type of trial (eg, parallel group, crossover, factorial, single group), allocation ratio and framework (eg, superiority, equivalence, non-inferiority, exploratory) Methods: participants, interventions and outcomes Study setting 9 Description of study settings (eg, community clinic, academic hospital) and list of countries where data will be collected; reference to where list of study sites can be obtained Continued http://bmjopen. bmj. com/ BMJ Open: first published as 10. 1136/bmjopen-2020-045105 on 30 June 2021. Downloaded from on July 12, 2021 by guest.
Modeling breeding values using the pedigree The survey is assumed to involve some genetic data in addition to the phenotypic data. These data may consist of a pedigree dataset, that indicates the parents and the date of birth of each individual. Without loss of generality, dates are expressed here in generations, like in  Wray (1990). This is more appropriate for populations with discrete generations, but any other time unit such as day or week could be used instead. The only restriction is that parents must appear in earlier time units than their offspring. The pedigree is assumed to have G + 1 generations, denoted by 0,. , G. In order to study the sensitivity of designs to mutation, breeding values are modeled taking account of mutation. The mutations arising at generation 0 are not taken into account since they are considered as being part of the alleles present at this generation.
Modeling breeding values using genotyping data Alternatively, the genetic data may consist of a genotyping dataset, giving the genotype of all the individuals at L biallelic loci. Let M be the N × L matrix that specifies which alleles each individual carries at each locus. Specifically, the genotype M il of individual i at locus l is set to 1 (respectively, -1) if individual i carries two copies of allele 1 (respectively, 2) at locus l, and to 0 if individual i is heterozygous at locus l  (VanRaden, 2008). The columns of M are often standardized  (VanRaden, 2008; Rincent et al. , 2017; Gianola et al. , 2020). As explained in the next paragraph, they are divided here by the square root of the average diagonal element of M M T , where T denotes transposition, yielding the matrix Z = M / √ z, where z = trace M M T /N.
Genetic algorithm. The D criterion can be maximized using the genetic algorithm STPGA  (Akdemir et al. , 2015; Akdemir and Isidro-Sánchez, 2019). This algorithm makes a population of designs evolve so that D values increase in the population over iterations. At each iteration, designs with large D values are selected in the population and produce offspring that forms the next generation. Offspring may not be identical to their parents because of mutation and recombination. Inefficient designs that were visited recently may be removed from the next generation (Tabu search). Designs generated using regression methods may be introduced in the next generation. This algorithm can be implemented using the function GenAlgForSubsetSelectionNoTest of the R package STPGA  (Akdemir, 2018). In this article, each design of the initial population is generated by SS independently (Section 3.