dataesr/scientific-paragraphs-categorization huggingface dataset:
Sinus graft infections are rare but serious complications, as they are associated with significant morbidity and sinus graft loss. The aim of this study was to systematically review the management of sinus graft infection in order to define which protocols should be implemented. The terms searched in each database were ''sinus graft infection management'', ''maxillary sinus lift infection'', ''maxillary sinus graft infection'', ''maxillary sinus elevation infection'', and ''maxillary sinus augmentation infection''. The management of the sinus graft infection was assessed. The outcomes evaluated were maxillary sinus health and dental implantation results. The initial search yielded 1190 results. Eighteen articles were included, reporting a total of 3319 patients and 217 sinus graft infections.
Materials and methods This systematic review was performed according to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines  13. The MED-LINE, Cochrane Library, and clinicaltrials. gov databases were searched. The terms searched in each database were ''sinus graft infection management'', ''maxillary sinus lift infection'', ''maxillary sinus graft infection'', ''maxillary sinus elevation infection'', and ''maxillary sinus augmentation infection''. There were no limits in the search, which was performed up to December 12, 2020. Studies were eligible if the management of sinus graft infection was assessed. The exclusion criteria were as follows: data mixed with other causes of sinusitis, case reports, technical notes, overviews, consensus statements, publication language other than English, and unavailability of the full text.
Results The initial search yielded 1190 results (Fig. 1 ). After the removal of duplicates (n = 447), 743 items were screened by title and abstract. Fifty-five publications were eligible based on their title and abstract, of which 37 were excluded: 26 case reports, two technical notes, three overviews, one consensus statement, one article in Russian, one article in Chinese, two unavailable full texts, and one article reporting mixed data of sinus graft infection with other causes of sinusitis. Finally, 18 articles were included  [14] [15] [16] [17] [18] [19] [20] [21] [22] [23] [24] [25] [26] [27] [28] [29] [30] [31]. A total of 3319 patients were included in these 18 articles, with 217 sinus graft infections. Two studies were prospective  17, 26  and 14 were retrospective  14- 16,18-25,28,29,31  ; two studies did not clearly state their design  27, 30  (Table  1 ).
ESSPRI was preferred to individual dryness scales because it is a validated score. The panellists selected the published minimal clinically important difference (MCID) as the response cut-off for clinESSDAI (≥3 points) and ESSPRI (≥1 point). Finally, the experts rejected the 'no worsening' clause because there is no published consensual definition for worsening of these outcomes and the options with this clause did not show better discriminative capacity (table  3 ). Finally, since the other 19 options (online supplemental 11) had good psychometric properties, they will be evaluated as exploratory endpoints in the NECESSITY clinical trial (EudraCT no: 2019-002470-32; online supplemental 12).
Acknowledgements We thank the  NECESSITY consortium  participants as well as the following experts:  Esen Karamursel Akpek ,  Alan Baer ,  Chiara Baldini ,  Elena Bartoloni ,  Marí-Alfonso Begona ,  Johan Brun ,  Vatinee Bunya ,  Laurent Chiche ,  Troy Daniels ,  Paul Emery ,  Robert Fox ,  Roberto Giacomelli ,  John Gonzales ,  John Greenspan ,  Robert Moots ,  Susumu Nishiyama ,  Elizabeth Price ,  Christophe Richez ,  Caroline Shiboski ,  Roser Solans Laque ,  Muthiah Srinivasan ,  Peter Olsson ,  Tsutomu Takeuchi ,  Frederick Vivino ,  Paraskevi Voulgari ,  Daniel Wallace ,  Ava Wu  and  Wen Zhang. We thank the anonymous patients from the  NECESSITY Patient Advisory Group  and the  Sjögren's Foundation  for their valuable contribution to the Delphi process. We thank  Dr EW St Clair  and  Dr AN Baer , who made publicly available the data of the baminercept trial.
Sjögren's syndrome Funding This project has received funding from the  Innovative Medicines Initiative 2 Joint Undertaking (JU)  under grant agreement number  806975. JU receives support from the  European Union 's  Horizon 2020 research and innovation programme and EFPIA. The present article reflects only the authors' view and JU is not responsible for any use that may be made of the information it contains. Disclaimer The views expressed in this publication are those of the authors and not necessarily those of the  NHS , the  National Institute for Health Research  or the Department of Health. Competing interests RS has received consulting fees from  GlaxoSmithKline ,  Boehringer ,  Janssen  and  Novartis , participated in an advisory board for  Janssen , and received support for attending meeting from  GlaxoSmithKline  and  Amgen.
METHODS The data that support the findings of this study are available from the corresponding author upon reasonable request. and fasting plasma glucose <126 mg/dL; (3) no history of myocardial infarction, congestive heart failure, stroke, or chronic inflammatory conditions based on participant self-report. The Columbia University and University of Minnesota institutional review boards approved the protocol. All participants provided informed consent. Only wave 2 participants who had 16S rRNA microbial data from subgingival plaque samples and were not missing important baseline risk factors or outcome data were included in this study.
16S rRNA Sequencing and Prediction of Gene Abundances Using PICRUSt2 We used 50 ng of DNA in polymerase chain reaction amplification targeting the V3 to V4 region of the 16S rRNA gene (using 341F/806R universal primers), and polymerase chain reaction products were purified using AMPure beads. 22, 23 For each library, 100 ng were pooled, gel purified, and quantified using a bioanalyzer, and 12 pM of the library mixture was spiked with 20% PhiX and run on a MiSeq (Illumina) platform. Raw reads were analyzed with QIIME2  24  (the data curation pipeline is outlined in Data S1). Overall, 44 776 283 sequences were generated for final analysis (median of 37 067 sequences per sample). We then used the bioinformatics tool PICRUSt2 18 through the qiime2 plugin, which uses the amplicon sequence variant relative abundance table, together with a reference database of known microbial genomes, to predict metagenomic content from 16S rRNA marker gene sequencing.
Limitations This study has some limitations. Associations observed between nitrite generation-to-depletion gene abundance and cardiometabolic risk met the FDR threshold for statistical significance in simple adjusted models, whereas in the fully adjusted models they only reached a P<0. 05 threshold, limiting how conclusive these findings are and highlighting the need for future replication. Our study used cross-sectional data, and reverse causation may explain our findings. It should be noted that the nitrite-generating and nitrite-depleting functional capacity in our population was inferred from 16S rRNA gene sequences and known bacterial genomes in available databases. Therefore, the findings still rely on taxonomic identified 16S rRNA marker gene sequences and do not reflect the true functional gene sequences from the microbiome.
The nitrite generation-to depletion ratios were most strongly positively correlated to A. naeslundii, C. durum, C. matruchotti, and R. dentocariosa (Figure  S3B ). Only Prevotella melaninogenica was consistently inversely associated with the gene abundance ratios (Figure  S3B ). Neisseria sicca was not present in this dataset and not included in the taxa summary score. In exploratory correlations of the taxa with individual gene abundances (Figure  S3C ), Rothia dentocariosa and Corynebacterium durum were the taxa most strongly associated with the nar and nir genes, and the narK nitrate/nitrite transporter gene. Hemophilus parainfluenzae had the strongest correlation with the nap nitrate reductase genes. Values presented in mean (SD) or n (%). P values are for ANOVA F statistics or X 2 tests for differences in level of covariates between participants across increasing quartiles of composite cardiometabolic Z-score (CMZ).
Acknowledgments The authors thank the following individuals for their invaluable contributions to this research:  C. Mclaughin ,  B. Batista , and  V. Rivera  for their assistance with recruitment;  R. Celenti  for her efforts in performing phlebotomy and processing and analyzing plaque samples;  A. Zuk  and  G. Zulaika  for their leadership and excellent program coordination; and  Drs Arora ,  Pokherel, Silfa , and Spinell for their skilled examinations and essential participant engagement. The authors are also profoundly grateful to the ORIGINS participants for their participation in this research.
NO 3 and NO 2 Transport Relative gene abundance refers to the proportion of individual KEGG Orthologs (KO) counts over the total number of functional gene counts in the sample. Absolute count data should not be directly interpreted as we do not have information about total microbial load and biospecimen collection volume was not standardized. Counts are presented only to illustrate the distributions of the different KOs. - narK K02575 0. 032667 12350 (9263) † KOs not detected in all participants; Prevalence of K15864 = 714 (93. 5%), K00367 = 477 (62. 4%), K00360 = 158 (20. 7%), K00372 = 18 (2. 4%). K17877 and K10534 were not present in the dataset. Abbreviations: NO 3 , nitrate; NO 2 , nitrite; NO, nitric oxide; NH 3 , ammonia; RD, respiratory denitrification; DNRA, dissimilatory nitrate reduction to ammonia; ANR, assimilatory nitrate reduction; KEGG, Kyoto Encyclopedia of Genes and Genomes; KO: Kyoto Encyclopaedia of Genes and Genomes Orthologs *.
Study Design and Subjects This observational study was conducted at the Oral Medicine Department of the Toulouse University Hospital Centre (France) during routine visits by three independent and calibrated experts. All patients attending a consultation were considered. To be eligible, the patients and/or guardians needed to understand French and to provide their consent for the data collection and clinical examination. If the oral clinical exam could not be performed, mostly because of lack of cooperation, the patient was excluded. All patients gave their consent. The personal and medical data were collected and computer-processed to analyze the results of this research.
Data Visualization, Modeling, and Explanation Different analysis strategies were successively combined to produce the final periodontal health prediction model. The machine learning pipeline (Figure  1 ) involved: (1) encoding binary and ordinal variables followed by a feature selection step, (2) a random training/test dataset splitting of 75:25, (3) a min-max data scaler followed by a multilayer perceptron model  [16] , and (4) explainability of the prediction results on the whole dataset (Figure  1 ). The scikit-learn library v0. 24. 0 was used as a general framework  [17]. BorutaPy v0. 3  [18]  is a feature selection method able to select a minimal set of features (i. e. , variables) that carry significant information for the prediction model. The following hyperparameters were used: 500 estimators, maximal depth of 3, and entropy as a criterion.
The profiles of the subjects, with respect to the variables selected by BorutaPy, were visualized by projecting them through UMAP (i. e. , uniform manifold approximation and projection  [19] ), followed by a DBSCAN algorithm using Euclidean distance to identify clusters of subjects. Descriptive statistics were then produced to characterize each cluster. After min-max normalization, the Boruta-reduced dataset was passed through a multilayer perceptron algorithm (from the scikit-learn library). Since this technique has some hyperparameters and we were seeking the best performing model with minimum overtraining, we explored the hyperparameter space using scikit-optimize v0. 8. 1. The best combination was retained: four hidden layers with 4, 128, 256, and 8 neurons, respectively, an Adam solver, an 'identity' activation function, 0.
7 beta1 and 0. 4 beta2 scores. To assess the performance of the model, we conducted a five-fold cross-validation, and compared the performance values (weighted F1-score  [20] ) between the training and validation sets. Finally, the complete performance of the model was assessed on the test set (precision, recall/sensitivity, specificity, weighted F1-score, and ROC curve) for each category to predict (CPITN 0, CPITN 1-2, and CPITN 3-4). The main obstacle to understanding most machine learning models is the "black box" aspect. Once a model has been trained, it is necessary to know the influences and interactions of the attributes behind the classification performed. Kernel SHAP is a modelagnostic method to approximate SHAP values  [12]. This method can explain the influence of each attribute of the dataset on the output of the predictive model.
Description of the Study Population A total of 532 subjects were examined between 02/01/2019 and 01/03/2021. The mean age of the total sample was 33 ± 15 years (range 2 to 83) with 45% of females (Table  S1 ). The distributions of the subjects' periodontal health scores by age group are detailed in Figure  S1. The maximum proportion of the healthy periodontium was found in the 0-10year-old group. The prevalence of gingival inflammation (i. e. , CPITN score 1-2) increased up until 35 years-old (20%, 40%, and 45% for the 0-10-, 10-20-, and 20-35-year-old groups, respectively) then decreased sharply after 35 years as periodontitis (CPITN score 3-4) increased. The missing data were encoded by assigning a "-1" value. By mapping all of the variables in two dimensions using an UMAP methodology, three distinct clusters were highlighted according to the sociodemographic characteristics and other risk factors of the individuals (Figure  S2 ).
Data Modeling by Machine Learning Models The machine learning pipeline consisted of a min-max scaler followed by a multilayer perceptron step, whose parameters had been tuned on the training data set. Using a five-fold cross-validation, the weighted F1-scores obtained for the training and validation datasets were 0. 60 ± 0. 03 and 0. 57 ± 0. 08, respectively. The confusion matrix (Figure  3A ) showed good prediction scores for healthy periodontium and periodontitis, but it highlighted some problems in the model for accurate prediction of gingival inflammation (final F1-score of 0. 74, 0. 32, 0. 68, and 0. 60 for CPITN 0, CPITN 1-2, CPITN 3-4 and average, respectively). The evaluation metrics presented in Supplementary Table  S2  and the ROC curve in Figure  3B  illustrate the specificity and sensitivity of the model prediction regarding each group prediction.
Discussion This study conducted a machine learning analysis based on an innovative strategy using a wide range of medical and sociodemographic parameters. The results support the hypothesis that, like many age-related inflammatory chronic diseases, periodontitis can be associated with a systemic risk profile, with no reference to oral stressors (e. g. , poor hygiene). Conversely, this kind of predictive pattern was not identified for gingival inflammation. This could be explained by the absence in the database of specific oral hygiene variables (e. g. , plaque index to highlight a putative poor hygiene) able to differentiate dental plaqueinduced gingival inflammations from those preceding periodontitis in susceptible hosts, which are largely uncorrelated with oral hygiene  [21]. All variables selected by the feature selection algorithm-i.
Acknowledgments: The authors thank  Aurélien Bos ,  Clothilde Broca ,  Sylvain Cussat-Blanc ,  Antoine Dubuc ,  Fanny Dudon ,  Audrina Fernandez ,  Caroline Gautier ,  Marie Piriou ,  Laura Rougemaille ,  Sophie Nguyen ,  Samia Sekkat , and  Chantal Soulé-Dupuy  for their help. The pictograms used in Figure 1 come from icones8. com.
Assessment criteria and evaluation method The periapical radiographs were independently examined by an experienced endodontist and a general dentist. Prior to the study onset, 120 periapical radiographs (not included in the analysis) were used to calibrate the examiners  (7). The reproducibility of the scores was assessed by Cohen kappa statistics (for inter-and intra-observer agreement) at day 0, month 1, and month 6. Kappa statistics for inter-observer and intra-observer showed near-perfect agreements (> 0. 81) (8). For each tooth, the following data were collected: i) the type of coronal restoration (direct lling, partial coverage crown, crown, temporary restoration, or none) and its quality (adequate vs. inadequate)  (9) ; ii) the presence and type of post (screw, metal, ber); iii) the Periapical Index (PAI)  (10) ; and, iv) the quality of the root canal lling.
Eligibility criteria An article was eligible whenever: i) the endodontic lling, the coronal restoration, and the periapical status have been evaluated by retroalveolar radiography; and, ii) it associated the evaluation of both the quality of endodontic treatment of permanent mature teeth and the quality of their coronal restoration. Articles in which root canal treatments were performed by specialists were not included. Meta-analyses and literature reviews were excluded. Titles and abstracts were screened. The references of retained articles were screened to identify additional articles. Useful full-texts were independently assessed by two practitioners. The data collected from each retained article were: i) article identi cation: authors and type, country, and year of publication; ii) participants: age, sample size; iii) methods: image acquisition, assessment of AP and coronal restoration, and statistical analysis; and, iv) results: total number of teeth, number of rootlled teeth, number of non-treated teeth, number of teeth with AP, number of teeth with GE and PE, and number of teeth with PR and GR.
Discussion This work attempted to determine which of two determinants (i. e. , radiological quality of the root canal treatment or sealing of the coronal restoration) has a more positive effect on the periapical status. It associated a cross-sectional study and a literature systematic review. Stemming from a much larger dataset, the sample size were limited, particularly those of subgroups, which would explain some unexpected results; e. g. , a high rate of periapical lesions in teeth with good endodontic treatment and good coronal restoration might be simply due to the small sizes of group GE + GR (only 17 cases). Otherwise, group GE + PR had less periapical lesions than group PE + GR (1. 1 times less) or PE + PR (1. 9 times less). This indicated that a 'diseased' periapical status would be more frequent after inadequate root canal treatment than after inadequate coronal restoration (37.
Acknowledgments The authors would like to thank all the investigators of hospital centers of RESTO DATA study:  Abid Sarah ,  Becavin Thibault ,  Becquart Mathilde ,  Berbel Fanny ,  Bertrand Marie-France ,  Blanc Cécile ,  Boudeau Mathilde ,  Bouyahmed Lydia ,  Canceill Thibault ,  Ceinos Romain ,  Collignon Anne-Margaux ,  David Alexandra ,  Decaup Pierre-Hadrien ,  Decerle Nicolas ,  Decup Franck ,  Domejean Sophie ,  Douchy Laura ,  Drouhet Thibault ,  Florimond Marion ,  Fouillen Kevin-John ,  Gasqui Marie-Agnès ,  Gastineau Richard ,  Gaucher Céline ,  Kerouredan Olivia ,  Khoury Claudine ,  Le Clerc Justine ,  Lefrançois Etienne ,  Lesieur Charlène ,  Maillet Christina ,  Mesgouez-Menez Catherine ,  Pérard Matthieu ,  Pradelle Nelly ,  Préaubert Estelle ,  Robberecht Lieven ,  Vallaeys Karen ,  Ziouani-Taïr Nawel.
Oral submucous fibrosis (OSF) is a chronic progressive condition affecting the oral cavity, oropharynx and upper third of the oesophagus. It is a potentially malignant disorder. The authors collated and analysed the existing literature to establish the overall malignant transformation rate (MTR). A retrospective analysis of medical and dental scientific literature using online indexed databases was conducted for the period 1956 to 2021. The quality of the enrolled studies was assessed by the Newcastle-Ottawa Scale (NOS). A meta-analysis using a random effects model of a single proportion was performed along with statistical tests for heterogeneity. The overall proportion of malignancy across all studies was 0. 06 (95% CI, 0. 02-0. 10), indicating an overall 6% risk of malignant transformation across all studies and cohorts.
Keywords Selection A systematic search of the scientific literature was performed for articles published from 1956 to 2021. The literature search was conducted in PubMed, Web of Science, PsycINFO, Google Scholar and the Cochrane Database/Cochrane Central using selected keywords based on the study objectives. A manual search of the literature was additionally undertaken and consisted of a reference list of the selected articles, and a reference list from recent systematic reviews on this topic. After removal of duplicate articles, 90 records in total were retained for screening. The detailed search strategy is available online: https: //www. crd. york. ac. uk/prospero/display_record. php?ID=CRD42021216333 (accessed on 23 January 2022) [Mylonas P, Albuquerque R, Murthy V, Yogarajah S, Carey B, Diniz-Freitas M, Monteiro L, Lodi G, Fricain JC.
Studies Selection and Data Extraction Process An updated database search was conducted by PM and VM. The filtering and screening processes were independently conducted by two investigators (SY and GL) and were supervised by the group leader (RA) who judged and resolved any discrepancies or disagreement. Two review authors (VM and PM) extracted data independently, using a standard data extraction process, and two further authors (RA and GL) cross-checked the resulting database entry against the full manuscript. Reviewer calibration was performed prior to data extraction by asking reviewers to extract data from specific articles and comparing the similarity and consistency of the extracted information. The calibration process involved primary training for the reviewers on the selection and data extraction of studies based on eligibility criteria.
Reviewers were asked to judge if a study was eligible or not based on a 20% sample of the studies. Once they had achieved an appropriate level of concordance (inter-examiner agreement of Kappa ≥ 0. 81), reviewers independently performed the screening processes. A similar process for data extraction was undertaken, with reviewers calibrating for data extraction on a 20% sample. Once they reached Kappa ≥ 0. 81, selection of the relevant data as highlighted on PROSPERO was carried out. The inclusion criteria for article selection included all prospective and retrospective cohort studies in any language, with clear details confirming the number of patients subsequently diagnosed with oral cancer. This included case series, meeting abstracts and clinical observational studies. Additional inclusion criteria included the number of patients recruited and patient ethnicity.
Summary Measures and Methods of Analysis All statistical analyses were performed using LibreOffice Calc v3. 6 software. The software was used to calculate descriptive measures including mean values for continuous variables. OSF cases were categorised for comparison purposes according to cohort ethnicity. Tests of significance such as the unpaired Student's t-test for comparing means were performed for the raw data sets; a value of p < 0. 05 was considered statistically significant. A meta-analysis using a random effects model of a single proportion was performed following statistical test for heterogeneity. Pooled results of the meta-analysis were performed for each subgroup (based on cohort ethnicity) and for the studies overall. The MTR was based on subgroup and overall group analysis. Confidence intervals were set at 95% and the weighting of each study was calculated as a percentage of all studies included in the meta-analysis.