dataesr/scientific-paragraphs-categorization huggingface dataset:
At 6. 5 months post (after both groups received psilocybin), antidepressant or anxiolytic response rates were approximately 60 to 80% measured with the Hospital Anxiety and Depression Scale (HADS) and the BDI. 77 A larger RCT of people with cancer, randomized participants to low dose first (n = 27; 1 or 3 mg/70 kg) or high dose first (n = 29; 22 or 30 mg/70 kg) of psilocybin in a cross-over design with 5 weeks between treatment sessions and a 6 month follow-up. Data from at least one session was collected from 51 participants, with 46 participants providing 6 month follow-up data. High dose first was superior to low dose first following the first treatment session and second dose was superior to first dose for low dose first following the second treatment session for measures of depression (Hamilton Rating Scale for Depression [HAMD], BDI, HADS) and anxiety (Hamilton Rating Scale for Anxiety [HAM-A], STAI-anxiety), and lower measures of depression and anxiety were sustained as 6 month follow-up for both groups.
82 his trial is subject to similar issues regarding blinding and expectancy noted above. A larger, multisite RCT of psilocybin for depression is currently in progress (ClinicalTrials. gov Identifier: NCT03866174) with no results available to date. Psilocybin with psychological support has been investigated in an open-labeled trial of patients (N = 20) with treatment-resistant depression (TRD) who received two doses (10 and 25 mg) 7 days apart. Participants were followed for 6 months and reported reductions in depressive symptoms measured using the QIDS-SR-16 at all posttreatment time points with the greatest improvement at 5weeks posttreatment. 83 sychological interventions assisted by psychedelic drugs has used psycholytic and psychedelic paradigms. Psycholytic approaches are associated with psychoanalytic practice and use low dose psychedelic drugs, especially LSD, to putatively reduce psychological defenses and to release unconscious information, whereas psychedelic approaches integrate the psychedelic experience into the psychotherapy session.
Purpose: Narcolepsy type-1 (NT1) is a rare chronic neurological sleep disorder with excessive daytime sleepiness (EDS) as usual first and cataplexy as pathognomonic symptom. Shortening the NT1 diagnostic delay is the key to reduce disease burden and related low quality of life. Here we investigated the changes of diagnostic delay over the diagnostic years (1990-2018) and the factors associated with the delay in Europe. Patients and Methods: We analyzed 580 NT1 patients (male: 325, female: 255) from 12 European countries using the European Narcolepsy Network database. We combined machine learning and linear mixed-effect regression to identify factors associated with the delay.
Results: The mean age at EDS onset and diagnosis of our patients was 20. 9±11. 8 (mean ± standard deviation) and 30. 5±14. 9 years old, respectively. Their mean and median diagnostic delay was 9. 7±11. 5 and 5. 3 (interquartile range: 1. 7-13. 2 years) years, respectively. We did not find significant differences in the diagnostic delay over years in either the whole dataset or in individual countries, although the delay showed significant differences in various countries. The number of patients with short (≤2-year) and long (≥13-year) diagnostic delay equally increased over decades, suggesting that subgroups of NT1 patients with variable disease progression may co-exist. Younger age at cataplexy onset, longer interval between EDS and cataplexy onsets, lower cataplexy.
Patients with relatively longer diagnostic delays were naturally excluded from those studies because they have not yet been diagnosed, ie, these "future patients" will be diagnosed later. Thus, whether the diagnostic delay of NT1 is really getting shorter over time is essentially unknown. Therefore, in this pan-European study we aimed to investigate 1) the diagnostic delay of NT1 in various countries, 2) the change of diagnostic delay over the past 30 years, and 3) factors associated with the diagnostic delay. We used the database of European Narcolepsy Network (EUNN), an association of 21 leading European sleep centers that launched the first prospective European web-based database for narcolepsy and other central hypersomnolence disorders. 3, 22, 23 The large number of patients across different European countries provided us an appropriate database to consider multiple contributing determinants.
Materials and Methods The EUNN database accommodates continuous large-scale standardized collection of 317 variables related to clinical symptoms (eg, EDS characteristics, cataplexy features, sleep drunkenness, sleep paralysis, and hypnagogic hallucinations), objective sleep-wake laboratory measures including polysomnography (PSG) and multiple sleep latency test (MSLT), biomarkers (eg, hypocretin-1 levels and HLA-DQB1*0602), subjective measures on sleep-wake patterns and quality, and demographics. 3 Each center of EU-NN obtained ethical approval for publishing the patients' data for scientific purpose by a national Institutional Review Board before entering patients' data. The scientific review committee of EU-NN approved the study protocol. All patients provided their informed consent for their data to be entered into the EU-NN database and for their data to be used for scientific studies.
Selection of Patients Patients with NT1 were diagnosed according to the ICSD-3. 4 We chose the year of EDS onset as disease onset, as EDS in general is the first symptom of narcolepsy to develop. The selection of patients is illustrated in Figure  2. The EU-NN database contains a variable on certainty of clinical diagnosis. The clinicians were asked to rate their diagnostic certainty on a 3-level scale (probable, possible, and definite). Only NT1 patients with definite diagnosis were included in this study. In rare cases patients may have EDS after diagnosis, ie, those patients may have other symptoms and/or low CSF hypocretin-1 concentration without EDS before diagnosis. Those patients were excluded from our analysis. Patients diagnosed before 1990s were also excluded considering the small number.
Null Hypothesis Testing Unlike the analyses done in previous studies that compared the delays between "patients starting EDS in different years", we chose to compare the delays between "patients diagnosed in different years". Such analyses have two advantages: 1) it is less influenced by selection bias because in reality sleep centers usually diagnose patients with different lengths of diagnostic delays in each year. The selection bias in previous studies can be best explained by giving an example. Assuming we aimed to compare the diagnostic delay of all NT1 patients who had symptom onset between 2016 and 2019, in a database of early 2021 the available data can only include those patients diagnosed by the end of 2020. In such a database, patients with symptom onset in 2016 may be diagnosed in any year between 2016 and 2020, resulting in diagnostic delays ranging from 0 to 4 years.
Short Delay vs Long Delay Non-parametric tests are usually less powerful to reject the null hypothesis (ie, higher risk of type II error) because they use less information from the data compared to parametric tests. 27 We hypothesized that the increases in the following two indices could indicate a decreasing trend in the diagnostic delay: 1) the proportion of patients with short delay in the whole database increased over years; and 2) the ratio of patients with short delay vs long delay increased over years. We correlated the two indices to the year of diagnosis using both Pearson and Spearman methods. A significant correlation (P-value<0. 05) indicated that the index increased over years.
Predictive Factors for Diagnostic Delay Selected by Machine Learning We applied machine learning to investigate the variables associated with the diagnostic delay. Stochastic gradient boosting (SGB) is a machine learning algorithm that iteratively creates weak single decision tree, and the tree ensemble can achieve the most accurate estimate of the response variable. It was demonstrated to be a suitable algorithm for modeling the EUNN database in our previous study. 22 e stratified the raw diagnostic delay into four subgroups in order to run the SGB model, ie, to convert the prediction of a continuous variable into a classification problem. The distribution of the raw data followed an exponential distribution and gave 39 outliers larger than 29. 4 years (Figure  3 ). Thus we did log-transformation to the raw data (ie, log-transformation of raw data +1 as raw data contained zero) and then divided the transformed data into four subgroups using the quartiles of boxplot (Figure  3 ).
DovePress Nature and Science of Sleep 2022:14 1036 diagnostic delay. The country difference was controlled by setting the country name as a random effect (ie, random intercept) in the LMM, and the fixed effects were the top-ranking predictors suggested by our SGB models. Only the countries with more than 10 patients of complete dataset of fixed effects were included into the LMM. Then stepwise regression using backward elimination was applied to the LMM to select the best predictors. We reported both the conditional R  230  and Ω 231 to assess the goodness of fit of our final selected models. All statistical analyses and modeling were performed using R (version 3. 5. 3). The SGB model was constructed with the R package caret. 32 The LMM model was done using the R package lme4 and lmerTest.
Country Differences The Wilcoxon rank sum test suggests no significant difference in the diagnostic delays between patients diagnosed in 2000s and 2010s in each country except for Finland (Table  1 ). Figure  6A  illustrates the delay of these 580 selected patients from 1990 to 2018 in each country and the results of post hoc pairwise comparisons. The diagnostic delay is significantly different among EUNN countries (Kruskal-Wallis rank sum test: P-value<0. 0001). Spain has longer delay compared to most of the other countries. The small number of patients in some countries may cause high Type II error in the results shown in Table  1  and Figure  6 , ie, low statistical power to reject the null hypothesis, so we did additional analyses using more patients from EUNN database. In these analyses we included non-validated patients, ie, patients with missing value in any of the mandatory fields but without missing data in the dates of EDS onset and diagnosis.
Results of Linear Mixed-Effect Model: Quantifying the Influences of the Predictors Country was the random effect in the LMM, and the other predictors listed in Figure  8  were fixed effects. The time interval between EDS onset and cataplexy was log-transformed, because as typical time-to-event data it follows exponential distribution, similar to the diagnostic delay. We excluded the year of EDS onset from the predictors because it strongly correlated to the diagnostic delay but had obvious selection bias. We subjected two datasets to LMM analysis, each one with and without PSG/MSLT parameters. The complete dataset including PSG and MSLT parameters contained 173 patients from France (n=16), Italy (n=66), the Netherlands (n=30), Spain (n=40) and Switzerland (n=21). The dataset without PSG and MSLT parameters included 407 patients from Finland (n=37), France (n=86), Germany (n=50), Italy (n=87), the Netherlands (n=45), Spain (n=75) and Switzerland (n=27).
1042 database. The mean and median diagnostic delay of our patients was about 9. 7 and 5. 3 years, respectively. 4] [35]  Our results show that the diagnostic delay remained stable in the past decades despite significant country differences. This means that our finding of the non-significant change in diagnostic delay is not due to the insignificance of specific countries that have larger number of patients in the database, but is a common phenomenon in EUNN countries. Combining machine learning (ie, stochastic gradient boosting) and linear mixed-effect regression analyses, we found that the occurrence of cataplexy (eg, age of cataplexy onset, delay between EDS onset and cataplexy onset, cataplexy frequency), severity of symptoms (eg, cataplexy frequency, duration of irresistible daytime sleep), REM sleep propensity, changes in nocturnal sleep duration, BMI and sex are significant predictors of diagnostic delay.
We believe that the results of the shortening of the time to diagnosis over the years reported in previous studies are strongly influenced by a selection bias because diagnostic delay was defined by the year of symptom onset. 7, 12, 17 Their results are confounded by the naturally decreased intervals between consecutive calendar years and the latest diagnostic year in their databases. Our new approach was less biased by choosing the year in which patients are diagnosed with a variation in time to diagnosis that remains stable over time. We illustrated this selection bias in our results in Figure  5A  by showing a clear decreasing trend in the diagnostic delay over the year of EDS onset, in which the maximal delay in each year can only stepwise decrease over time because it cannot exceed the time interval between the year of EDS onset and the latest diagnostic year in the database.
The increased number of NT1 patients with short diagnostic delay following 2009-2010 H1N1 pandemic,  5, 6, 23, 36  and the recent evidence of immune-mediated NT1 cases  1, [37] [38] [39]  are two milestones in narcolepsy in the last decade. The increased number of acute NT1 patients in 2010s may lead to the impression that the diagnostic delay is getting shorter. Also, our result indeed confirms an increased number of patients with short diagnostic delay in 2010-2013. However, the fact that the number of NT1 patients with long diagnostic delay was also increased simultaneously contradicts the notion of shorter diagnostic delay. Likewise, the proportion of patients with short delay and the ratio between short delay and long delay did not show an increasing trend in the past decades. Finally, the comparisons of the stratified diagnostic delay across 3 different decades (1990s, 2000s and 2010s) did not show significant difference in the whole EUNN database either.
58, 59 Unfortunately, in the current EU-NN database we have not asked for the reasons of patient referral to the sleep lab. Future studies should take these reasons into account to better understand the reasoning behind delayed diagnosis of narcolepsy. The EU-NN database allows us, for the first time, to compare the diagnostic delays of NT1 among 12 European countries. As a multi-center, multi-country study, it has several limitations. First, not all patients have been included from all centers yet, and systematic errors in epidemiological research such as recall bias could also exist in our study. Second, it is hard to match the number of patients between different centers/countries. The numbers of patients from some countries were too small to be included in some of our analyses, eg, the results shown in Table  1.
Acknowledgments The EU-NN database is financed by the  EU-NN. The  EU-NN  has received financial support from  UCB Pharma Brussels  for developing the EU-NN database. We thank  Teresa Paiva ,  Peter Young ,  Johannes Mathis ,  Michel Lecendreux ,  Christian R. Baumann ,  Carole Pesenti ,  Alex Iranzo ,  Laura Lillo-Triguero ,  Pablo Medrano-Martínez ,  Corina Gorban  and  José Haba-Rubio  who helped to input the patients' data into EU-NN database.
2: Dataset without PSG and MSLT variables MSLT is short for multiple sleep latency test, PSG is polysomnography, REM is rapid eye movement. Delay between EDS onset and cataplexy onset 0. 49 0. 042 11. 58 <0. 0001 Cataplexy frequency -0. 15 0. 036 -4. 15 <0. 0001 Sex: Male -0. 24 0. 083 -2. 87 0. 0043 BMI 0. 039 0. 007 5. 46 <0. 0001 Note:https://doi. org/10. 2147/NSS. S359980DovePressNature and Science of Sleep 2022:14.
Method Participants. This study was not pre-registered. Participants were from the Health and Retirement Study (HRS), the National Social Life, Health, and Aging Project (NSHAP), the Wisconsin Longitudinal Study graduate (WLSG) and sibling (WLSS) samples, the National Health and Aging Trends Survey (NHATS), the Longitudinal Internet Studies for the Social Sciences (LISS), and the English Longitudinal Study of Ageing (ELSA). In all cohorts, participants provided written informed consent, and all cohorts were conducted according to the principles expressed in the declaration of Helsinki. The present study was exempted from Institutional Review Board review because it used de-identified publicly available datasets. Descriptive statistics for the seven samples are presented in Table  1. The HRS is a nationally representative longitudinal study of Americans older than 50 years and their spouse.
Half of the sample provided demographic factors, personality, and fatigue data in 2006, and the other half The WLS is a long-term study of a random sample of 10,317 men and women who graduated from Wisconsin high schools in 1957, which is broadly representative of white, non-Hispanic American men and women who graduated from high school in Wisconsin at the time (WLSG). Baseline demographic, personality, and fatigue data were obtained in 1992-1993 from a total of 6,590 participants aged from 51 to 56 years old (54% women, mean age = 53. 21, SD = 0. 62). Of this sample, 4233 participants provided follow-up fatigue data in 2011. Selected siblings of some of the graduates were also included in the WLS (WLSS). A total of 3356 participants aged from 29 to 79 years old (53% women, mean age = 53. 51, SD = 7.
38) provided complete baseline data in 1993-1994. From this sample, follow-up fatigue data were obtained from 1948 participants in 2011. A public use file of data is available at http:// www. ssc. wisc. edu/ wlsre search/ data/. The WLS received approval from the Health Sciences IRB at University of Wisconsin-Madison. The LISS is a representative longitudinal sample of the Dutch population. Complete baseline personality, demographic, and fatigue data were obtained in 2007 from a total of 5,796 participants aged from 16 to 94 years old (54% women, mean age = 45. 98, SD = 15. 66). Within this sample, follow-up fatigue was obtained in 2020 from 1,903 individuals. Information about the LISS panel can be found at: www. lissd ata. nl. Ethical approval for the the LISS panel was given by the board of overseers.
ELSA is a representative cohort of men and women living in England aged 50 years and older. A sample of 8078 individuals aged from 50 to 89 years (55% women, mean age = 66. 08, SD = 8. 68) provided complete baseline demographic, personality, and fatigue data at wave 5 (2011). From this sample, a total of 5093 participants provided follow-up fatigue data at wave 9 (2018). Information about ELSA can be found at: https:// www. elsa-proje ct. ac. uk/. ELSA was approved by the National Research and Ethics Committee of the UK National Health Service. The NHATS is a nationally representative longitudinal study of Medicare enrollees aged 65 years and older. Personality was first assessed in 2013 for one-third of the sample, and in 2014 for a second third. Both waves were combined. A sample of 2760 participants aged from 67 to 89 years (59% women, mean age = 79.
47, SD = 7. 53) provided complete data on demographic factors, personality, and self-rated health at baseline. Follow-up fatigue data was obtained in 2020 from 1,270 individuals. NHATS data are available at: https:// nhats. org/. The NHATS was approved by the Johns Hopkins Bloomberg School of Public Health Institutional Review Board. 41 in the HRS, ELSA, NSHAP, and NHATS. A 26 version of the MIDI was used in the HRS and ELSA, a 21-item version was used in the NSHAP, and a 10 item version was used in the NHATS. They were asked to indicate on a scale ranging from 1 (not at all) to 4 (a lot) how well adjectives assessing the five traits described themselves. Example adjectives are: worrying (neuroticism), talkative (extraversion), creative (openness), caring (agreeableness), and organized (conscientiousness).
Data analysis. All analyses were conducted for each trait and in each sample separately and the results combined with random-effect meta-analyses using the Comprehensive Meta-Analysis software (Version 2)  50. The random-effects method was used based on the assumption that the underlying true effects may differ across samples. Heterogeneity was assessed using the I 2 indicator  51. Personality traits were z-scored (Mean = 0 and SD = 1) to report all personality effects as a 1 SD difference. In cross-sectional analyses, logistic regressions were conducted to test whether personality traits were related to the risk of concurrent fatigue at baseline. In longitudinal analyses, logistic regressions were conducted to test whether personality traits predict incident fatigue at follow-up among individuals who did not report fatigue at baseline (individuals who reported fatigue at baseline were excluded from this analysis).
Data availability The Health and Retirement Study (HRS) is sponsored by the National Institute on Aging (NIA-U01AG009740) and conducted by the University of Michigan. HRS data is publicly available at http:// hrson line. isr. umich. edu/. The Wisconsin Longitudinal Study (WLS) has been supported principally by the National Institute on Aging (AG-9775, AG-21079, AG-033285, and AG-041868), with additional support from the Vilas Estate Trust, the National Science Foundation, the Spencer Foundation, and the Graduate School of the University of Wisconsin-Madison. A public use file of data is available at http:// www. ssc. wisc. edu/ wlsre search/ data/. The Longitudinal Internet Studies for the Social Sciences (LISS) panel data were collected by CentERdata (Tilburg University, The Netherlands) through its MESS project funded by the Netherlands Organization for Scientific Research.
More information about the LISS panel can be found at: www. lissd ata. nl. Funding for the English Longitudinal Study of Ageing is provided by the National Institute of Aging [Grants 2RO1AG7644-01A1 and 2RO1AG017644] and a consortium of UK government departments coordinated by the Office for National Statistics. ELSA data are available from the UK Data Service (UKDS, https:// www. ukdat aserv ice. ac. uk/). The National Health, Social Life and Aging Project (NSHAP) is supported by the National Institutes of Health, including the National Institute on Aging, the Office of Women's Health Research, the Office of AIDS Research, and the Office of Behavioral and Social Sciences Research (Grants R01 AG021487, R37 AG030481, R01 AG033903, R01 AG043538, and R01 AG048511). Information on how to access the NSHAP data can be found at: http:// www.
An approach for answering clinically relevant decision questions In the following, we propose a stepwise approach to express clinically relevant decision questions as hierarchy questions and quantify the uncertainty of the criteria that constitute them. We have developed an R package nmarank, hosted in CRAN  [20] , which allows users to implement the suggested approach  [21]. In  [22]  readers can find the current version of the nmarank package which can be used to reproduce the results presented in the manuscript. The documentation of the package version 0. 2-3 serves as a guide of the functions included in the nmarank package. The suggested approach is outlined below.
Limitations The proposed method has several limitations. In decision making multiple outcomes are often of interest and the current approach cannot take this into consideration. Moreover, benefit-risk assessments between different outcomes may be of interest to decision makers but cannot currently be appropriately handled. It would be potentially helpful that the derived frequencies are accompanied by 95% confidence intervals; as, however, the interpretation of such intervals would be unclear, they are not incorporated in the current version of the nmarank package  [21].