dataesr/scientific-paragraphs-categorization huggingface dataset:
Information sources Literature database and pragmatic searches were conducted with tailored pre-determined search strategies (Supplementary Table  1 , Supplementary Table  2 ) with additional searching of reference lists from published key SLRs. Further details can be found in the Supplementary Methods. Studies eligible for inclusion were RCTs published between 1 st January 1998 and 3 rd August 2022, recruited patients aged ≥12 years with moderate-severe asthma, contained ≥1 regular dosing or MART treatment arm, and assessed the efficacy of these treatment arms through ≥1 outcome of interest. Trials of patients without asthma, aged <12 years were excluded as well as studies not assessing regular dosing/ MART or reporting outcomes of interest (Table  1 ). Traits and outcomes of interest were those deemed most clinically relevant by a panel of experts with clinical experience in treating moderate-severe asthma.
Data collection All records were managed within Excel (Microsoft Office) and the PICO Portal which identified and merged duplicate records, with manual verification. The study was employed as the unit of interest to prevent the double counting of patients from studies with multiple reports. Data varying between treatment arms was individually retrieved and all data were extracted as reported by the original authors. A single reviewer conducted all initial data extraction with validation by a second independent reviewer. A third reviewer verified the final dataset. Summary statistics were extracted for continuous measurements and frequencies for dichotomous measures. Graphical results were digitalised with DigitizeIt software (I. Bormann, Braunschweig, Germany)  [11]. Trial data extracted included the study population, treatment arms, participants' baseline characteristics and clinical outcomes of interest.
Meta-analysis 2. 2. 1. Feasibility assessment and synthesis methods The meta-analysis of data extracted from the SLR consisted of hierarchical network meta-regressions (NMR). Eligible studies identified by the SLR: i) assessed patients with moderate-severe asthma, and ii) reported the outcome of interest for the treatment arm of interest, and iii) at least one treatable trait at baseline (Table  1 ). Moderate-severe asthma was determined based on original authors' assessment of severity; where this was not reported, severity was estimated from reported medication doses and classified based on GINA recommendations  [2]. Studies which did not separate patients by severity of disease were included in the moderate-severe asthma analysis. For inclusion in each model of the NMA, each outcome and patient trait assessed needed to be reported in ≥5 RCTs by the same mode of measurement (Table  2 )  [13].
Statistics Summary statistics are reported for patient demographics and clinical outcomes of each study in the SLR. Continuous data are given as the mean of all participants in the study, discrete data are given as the percentage of participants in each study. Results of the NMA are summarised by β PRD (95% credible interval [CI]; where PRD [proactive regular dosing] here refers to the regular dosing regimen), which represents the interaction of the patient trait with the treatment effect of regular dosing relative to MART, measured by the clinical outcome. Binary outcomes are expressed as log-odds ratio (OR), event outcomes are expressed as log-hazard ratio (HR), and continuous outcomes are expressed as between-group mean differences.
Exacerbation rate A total of 22 RCTs were used in the models assessing the influence of patient traits on treatment effects on exacerbation rate. Excluded studies did not report exacerbation rate data at or after 6 months or contained similar treatment strategies (medium-dose ICS/LABA regular dosing). All 22 studies were included in the model assessing the effect of age, 16 on baseline ACQ score, 14 on both smoking and baseline FEV 1 %, and nine on BMI (Supplementary Table  6 ). The evidence network for this NMA is presented in Supplementary Fig. 3. No statistically significant interaction between any trait and relative treatment effect of regular dosing compared with MART as measured by exacerbation rate was observed (Fig. 1 ). Non-significant decreases in the logOR of exacerbation rate were seen with increasing baseline FEV 1 % with regular dosing and towards MART for ever-smokers.
Time to first exacerbation A total of 20 RCTs were used in models assessing the influence of patient traits on regular dosing treatment effects on the time to first exacerbation relative to MART. Excluded studies did not report either information on time for first exacerbation by the end of the study or at baseline/change from baseline. Data from 20 studies were included in the model assessing the effect of age on treatment efficacy, 15 in the model assessing baseline ACQ, 14 in the models assessing smoking and baseline FEV 1 %, and 10 in the model for BMI (Supplementary Table  7 ). The evidence network for this NMA is presented in Supplementary Fig. 4. There was no statistically significant interaction between any trait and relative treatment effect of regular dosing compared with MART as measured by time to first exacerbation (Fig.
Lung function Data from 18 RCTs were used in models assessing the influence of patient traits on treatment effects on lung function, defined as mean change in FEV 1 (L) from baseline. Excluded studies did not report change from baseline FEV 1 (L) data at or after 6 months and one study was excluded due to identical treatment arms. The model assessing the effect of age used all 18 studies, 17 were used in the model assessing the effect of baseline FEV 1 %, 14 in the model assessing baseline ACQ score, 11 studies were included in the model on smoking, and eight were used in the model assessing the effect of BMI (Supplementary Table  8 ). The evidence network for this NMA is presented in Supplementary Fig. 5. There was no statistically significant interaction between any trait and relative treatment effect of regular dosing compared with MART as measured by change from baseline FEV 1 (Fig.
Reliever use Data from a total of 17 RCTs were used in models assessing the influence of patient traits on treatment effects on reliever use. Excluded studies did not report information on reliever use by the end of the study or baseline/change from baseline reliever use. All 17 RCTs were used in the model assessing age. Data from 14 studies were included in the model assessing baseline FEV 1 % and ACQ score, 12 studies in the model assessing smoking, and eight studies in the model assessing BMI (Supplementary Table  9 ). The evidence network for this NMA is presented in Supplementary Fig. 6. There was no statistically significant interaction between any trait and relative treatment effect of regular dosing compared with MART as measured by reliever use (Fig. 4 ).
The current study, which included RCT data from all commonly used ICS/LABA molecules, was unable to identify any significant influence of a patient trait on the effect of MART and could not find any difference in the odds ratio on exacerbation rate for individual patient traits. An interesting finding was that there was no significant effect of patient traits on reliever use between regular dosing compared with MART for the studied traits. However, some non-significant trends within the data were observed, suggesting further research may reveal new insights. Further analysis is needed to assess these trends. Observational/real-world data from sources such as  medical databases and registries may provide datasets with sufficient statistical power to identify the influence of the patient traits described here and additional traits on treatment efficacy.
However, these analyses rely on individual, high quality research with comparable datasets. The lack of consistency between RCTs in respiratory medicine limits the ability of meta-analyses to accurately identify best clinical practices  [18]. Strengths of this NMA include that the SLR identifying the RCTs was conducted using robust methodology and that a formal feasibility assessment was used to determine which trials were eligible for inclusion. The patient traits and outcomes of interest were chosen based on feedback from clinical experts with experience in treating patients with asthma and so are likely to be relevant to real-world clinical practice. Limitations of this study include that models were based on relative treatment effects for regular dosing vs MART, and therefore do not aid inferences on which patients are more likely to experience outcomes irrespective of treatment received.
Acknowledgements The authors would like to thank  Daniel Stellato ,  Tom Delea , and  Carissa Esober Doban  from  Policy Analysis Inc. , and  Aditya Joshi  from  GSK  for providing statistical support;  Mareva Faure ,  Cloé Esposito , and  Avril Gouault Lalibetré  from  Peripharm, Montreal, Canada  for providing support in conducting the SLR; and  Sourabh Fulmali ,  GSK , for providing support throughout the study. Editorial support (in the form of writing assistance, including preparation of the draft manuscript under the direction and guidance of the authors, collating and incorporating authors' comments for each draft, assembling tables and figures, grammatical editing and referencing) was provided by  Katy Widnall, PhD , of  Fishawack Indicia Ltd, UK , part of  Fishawack Health , and was funded by  GSK.
Study Design The "Endocare for Pelvic-perineal Pain Related to Endometriosis Used at Home" study was a randomized, sham-controlled, double-blinded, two-parallel-group, single-site, interventional clinical trial evaluating the efficacy of software as a medical device (SaMD) product on reducing endometriosis-related pain at home compared to a digital control. The study was conducted between December 2021 and October 2022 at the Franco-European Multidisciplinary Endometriosis Institute, Tivoli-Ducos Clinic in Bordeaux, France.
Study Devices Endocare is a class I software medical device developed by Lucine (Bordeaux, France) associated with a VR headset intended to decrease pelvic pain related to endometriosis. The software consists of a 20-minute treatment combining auditory and visual therapeutic stimulations integrated in a 3D VR environment, including binaural beats, verbal hypnotic injunction, nature-based sounds, distraction of attention, and bilateral alternative stimulations. The digital control (control) was also developed by Lucine as a 20-minute audio-video composition similar to Endocare (same context, environment, and duration) with exposure to nature sounds, but without Endocare's stimulations, as previously described  [24]. The sham control and Endocare groups used the same hardware: Oculus Quest 2 VR headsets and AKG K-240 MKII audio headphones.
Ethical Considerations The study was conducted in compliance with the 1964 Declaration of Helsinki and later amendments, ISO 14155:2020, Medical Device Regulation (EU) 2017/745, and ICH-GCP E6. The study received approval on October 26, 2021, by the Comité de Protection des Personnes, Région Ouest, Ouest 1 (approval number 2021-A02358-33). All patients signed an informed consent form before inclusion and performing any study-related procedure. The study is registered at ClinicalTrials. gov under registration number NCT05172492.
Impact on Medication Use During their participation, patients were free to use concomitant rescue pain medications as usual considering that the device efficacy may differ between participants. The objective was to evaluate patients' adherence to a DTx, the integration of the device in their daily routine care, and its impact on pain medication intake. Unfortunately, only short-term data were collected and additional investigations would be needed to also demonstrate a long-term impact of the DTx on long-term medication use habits. Nevertheless, at equivalent medication use between groups, higher pain reduction was observed with Endocare. Compared to baseline levels, patients declared increased use of medications. This was an expected result, as day 1 represents the patient's first day of painful periods when they need pain relief the most.
Conclusions This is the first study to report the home use, without medical supervision, of a VR solution designed to reduce pain in patients suffering from chronic pelvic pain associated with endometriosis. Although the study has some limitations due to its conduct at patients' homes and not in a hospitalized, controlled environment, the results clearly demonstrated the feasibility and effectiveness of VR software use without medical supervision. The decrease of analgesic use and of pain intensity observed in the study provides evidence for the integration of VR in multimodal chronic pelvic pain treatment strategies and may help to avoid the potential opioid dependence observed in the long-term treatment of chronic pain. Long-term data would enable collecting more information about the effect of Endocare on analgesics use.
Apart from wet-lab based methods for contamination control (e. g. , experimental methods), tools such as DeconSeq  14  or Recentrifuge  15  have digital procedures to remove genomics sequences that correspond to negative control samples. They are however not tailored for aDNA and require either a database of negative controls or an index of reference genomes to distinguish the contamination that should be removed from endogenous material. Moreover, due to the nature of the biosamples processed in aDNA studies, researchers face the challenge of having small sample sizes, a typical feature of the ancient metagenomics field that often leads to underpowered studies. 13  In principle one could also perform read decontamination by read mapping, for instance, to a database of oral microbiota reference genomes while keeping only the reads that align with sufficient identity.
However such a reference database does not exist, and the diversity of ancient oral microbiomes is not yet well characterized. 16  Alternatively, one could decontaminate a sample by taking out reads that align to a database of known contaminant reference genomes, such as soil and skin microbes-however no such database exists and is unlikely to be created given the extensive diversity of these environments. 17  Hence, mapping-based approaches are currently unsuitable for the decontamination of ancient metagenomes, and one must rely on alternative approaches, such as the one presented here. Terabytes of ancient metagenomic data exist in public repositories, and also petabytes of metagenome data have been produced over diverse environments. As an attempt to globally make this huge amount of data accessible, bioinformaticians have developed efficient algorithmic methods to aggregate substrings of genomic sequences of length k, called k-mers, within these collections.
Using tools such as kmtricks  18  one can rapidly construct a matrix of k-mers from large metagenomic collections, allowing to jointly analyze all k-mers present within hundreds to thousands of metagenomic samples. However, such aggregation of k-mer information over hundreds of metagenomes has never been applied to the problem of decontaminating aDNA reads yet. We developed aKmerBroom, the first method able to decontaminate ancient oral DNA samples without the need for a control sample nor an extensive set of reference genomes. Our method leverages the wealth of existing ancient oral metagenomes by constructing a database of ancient oral k-mers used to capture reads likely to be of ancient oral origin. In essence, aKmerBroom projects the k-mers from an input sample onto a database of reference k-mers and then selects the reads with enough coverage.
26  Yet we are not aware of tools developed specifically for contamination removal in ancient oral DNA at the read level, despite this sample type being one of the most prevalent source of aDNA. DeconSeq,  14  published in 2011, is a method built to detect and identify contamination in microbial metagenomes. 27  It takes as input a set of reads, and compares it against a reference database using a modified version of the BWA-SW algorithm. 28  DeconSeq uses different databases depending on whether the user wants to remove or retain reads. None of the databases were built for ancient oral metagenomic decontamination. The user might create their own ancient index for contaminant screening but this requires having a reference of control samples and increases the running time for contamination removal.
e. , DNA exchange between samples within one same study that can create batch effects. 29, 32  It is based on Centrifuge,  33  a taxonomic classifier that uses the Burrows-Wheeler Transform (BWT) and an FM-index to store and index a reference database. Recentrifuge reads the score given to the reads by a taxonomic classification software (such as Centrifuge), and uses this information to calculate an average confidence level for each taxon in the taxonomic tree associated with the sample analyzed. Tools of this kind rely on sequencing blank samples (controls) to determine baseline contaminant levels of microbes. To summarize, existing methods are not tailored for the decontamination of ancient oral metagenomic projects, as they are referencebased, and have not been recently updated to scale up to modern dataset sizes (such as DeconSeq) or rely on the sequencing of controls which has limited uses (such as microDecon or Recentrifuge).
Datasets To evaluate aKmerBroom in a controlled setting with known levels of contamination, we constructed three distinct synthetic datasets, corresponding to various scenarios. The Synthetic 1 and Synthetic 2 datasets correspond to the case where all or part of the reads observed in the target sample are from samples used to construct the trusted oral k-mer set, and are thus easier to decontaminate. The Synthetic 3 dataset corresponds to a case where we observe a completely new and unseen sample. Each dataset is built with an equal number of reads belonging to each of the three categories aOral, Sediment/Soil, and Skin, in a 1=3: 1=3: 1=3 proportion. We also used two real datasets from an ancient oral microbiome study. Table  1  presents the datasets.
Synthetic 1: We collected 2 million reads from a source soil dataset, 2 million reads from an aOral sample and 2 million reads from a skin sample. All these source samples were present in the k-mer matrix used to create the trusted k-mers set, hence this is a best-case scenario for decontamination. Synthetic 2: A second dataset was built by sampling 2 million reads from an external aOral sample that was not used to create the trusted k-mers set. We added the 2 million reads from the skin and sediment/soil datasets used for Synthetic 1. Hence this dataset is a semi-artificial best-case scenario. Synthetic 3: A third and final synthetic dataset was built by sub-sampling reads from aOral, soil and skin datasets that were not used to create the trusted k-mers set. For the construction of this dataset, 2 million reads were sub-sampled from am aOral sample, a sediment/ soil sample, and a skin sample, respectively.
Real data: Lastly, we evaluated decontamination on two real datasets: First, an aOral sample (accession SRA:ERR5670971), isolated from Trentino-South Tyrol, Italy, and dating from the Early Middle Ages (400-1000 CE). Second, a real dataset (accession SRA:ERR5670966) isolated from Venosta Valley and dating from the Early Middle Ages too. 16  None of these datasets were used to create the set trusted k-mers. A negative control sequenced and published in the same study was used to run Recentrifuge and DeconSeq, but not aKmerBroom.
Evaluation method As we know the exact number of reads coming from the aOral sample in each of the three balanced synthetic datasets, we estimated specificity and sensitivity by calculating the true positive rate (TPR) and false positive rate (FPR). We considered as true ancient oral any read recovered by aKmerBroom coming from the aOral samples, and considered as false aOral the reads coming from the soil/skin samples. On the other hand, as we do not know the true number of contaminant reads for the real dataset, we evaluated performance by measuring read retainment, that is the percentage of original reads that were kept after contamination removal. Competing decontamination methods such as Recentrifuge and DeconSeq were only evaluated on real data since they require negative controls or reference databases which were not available for our 3 synthetic samples.
Recentrifuge relies on Centrifuge  33  for taxonomic classification of an input set of reads. We used Centrifuge version 1. 0. 4-beta on a pre-made index of RefSeq bacteria, archaeal, viral, and human sequences. 34  DeconSeq standalone version 0. 4. 3 was used for performance comparison against aKmerBroom on real data. To evaluate the composition of the samples before and after decontamination, we performed a contamination assessment with SourceTracker and using as sources our reference database of 360 metagenomic samples.
Evaluation of decontamination on synthetic data Table  2  reports that aKmerBroom has excellent performance (R 93% sensitivity and specificity) on synthetic datasets 1 and 2. Synthetic dataset 3 was built by sub-sampling from datasets that were not seen during construction of the trusted k-mers set, hence it is a more realistic case. Here aKmerBroom still performs remarkably well with 89:57% sensitivity and 94:00% specificity, albeit shows lower sensitivity than in the first two synthetic datasets. Contamination assessment analyses using SourceTracker (Figure  2 ) show that after decontamination with aKmerBroom the final oral composition is above 80% in the three synthetic datasets. This proves that also with alternative metrics to sensitivity and specificity, such as source environment proportions given by MST analyses, our method performs contamination removal effectively.
Evaluation of decontamination on real data When evaluating aKmerBroom on real data, we measured performance with read retainment and compared results with two competing methods: DeconSeq and Recentrifuge. We took two aOral metagenomic samples isolated from the dental calculus microbiome of two people buried in Italy in the Early Middle Ages (400-1000 CE). 16  Researchers of this study also published a sequenced blank, which we used to create the database for contaminant screening and run DeconSeq with. That same blank was used as negative control when running Recentrifuge, in order to have a reference of taxa that needs to be removed. The group that collected, sequenced, and published those real datasets performed several aDNA authentication analyses to prove their samples were representative of the ancient calculus microbiome.
Among others, they ran SourceTracker  35  on the aOral samples and showed that the reads stemming from a known source were predominantly coming from modern calculus and plaque,  16  i. e. , oral sources. Thus we expect a highly reliable ancient oral content in the real sample evaluated, and a low level of contamination. For this reason we used read retainment and confirmed that aKmerBroom preserves most of the reads of the original aOral sample ð92:56%Þ, whereas Recentrifuge and DeconSeq remove most of the sequences (see Table  3 ). We additionally performed an evaluation of the real samples of ancient oral origin, by running mSourceTracker on each of the samples and against a set of sources represented with an OTU table built from our reference collection of 360 metagenomic samples (sources: ancient oral For the real dataset, the accession reported for the aOral source corresponds to the sample likely to contain ancient oral microbes, to be decontaminated.
The sample reported in the real datasets Sediment/Soil source is a negative control. Sensitivity is the percentage of aOral reads that were successfully retained. Specificity is the percentage of non-aOral reads that were successfully removed. [aOral], modern oral [mOral], Sediment/Soil and Skin) (further details on the OTU table construction and taxonomic classifier used are detailed in the Supplementary material of decOM  36  ). Results are presented in Figure  3.