dataesr/scientific-paragraphs-categorization huggingface dataset:
For the analyses of preterm birth, we followed women from gestational week 22 until an outcome event or censoring at a gestational age of 259 days (37 weeks). We considered women to be exposed from the day in pregnancy when a neuraminidase inhibitor was dispensed and to remain as such until delivery. We adjusted all outcomes for country (Scandinavian database only), year of birth, maternal age, maternal comorbidity, and smoking. We excluded women from the adjusted analysis if they had missing data on any of the five covariates. For malformations, we present the adjusted odds ratios only from analyses with more than five births among exposed infants with the specific malformation or organ system event. To assess the robustness of the logistic regression model and to investigate whether including body mass index in the model affected the results, we did a restricted analysis including only data from Sweden and Denmark, both of which had nearly complete information on this variable.
As inclusion of body mass index in the analysis carried out in the subset did not confound the results, we did not consider this variable in further analyses. To combine the adjusted results from the Scandinavian and the French databases, we did a meta-analysis using the Metan command (version 9) in Stata (version 11. 2). 21 22 We estimated a random effect model. We used the χ 2 test for heterogeneity and the I 2 statistic to assess the heterogeneity between the Scandinavian and French data.
81, 0. 51 to 1. 30). The analysis of neonatal morbidity included only Scandinavian data. We found no overall association between exposure to neuraminidase inhibitors and increased neonatal morbidity during the first 28 days of life (adjusted odds ratio 0. 92, 0. 86 to 1. 00) (table  3 ). In the adjusted analysis, we found a decreased risk of respiratory and cardiovascular disorders specific to the neonatal period among infants exposed to neuraminidase inhibitors during pregnancy (adjusted odds ratio 0. 84, 0. 75 to 0. 95). The estimates of risk for the neonatal outcomes by trimester of exposure and database are shown in supplementary table C. When we included women exposed to only oseltamivir (supplementary table D), the point estimates and 95% confidence intervals for all the estimated outcomes were similar to those derived from the compound exposure (supplementary tables E-H and supplementary figure  B ).
Congenital malformations The analysis of congenital malformations included only Scandinavian data from infants exposed during the first trimester, as no congenital malformations were reported in the 95 infants exposed during the first trimester in the EFEMERIS database. Of the 1125 infants exposed in the first trimester, 44 had at least one congenital malformation diagnosed, yielding a prevalence of 3. 9%. In the unexposed group, 19 509 (2. 9%) of the 672 784 infants had at least one congenital malformation. Among the exposed infants, three (0. 3%) had more than one malformation. The corresponding number for the unexposed infants was 2336 (0. 3%). In the adjusted analysis, we found no increased risk of congenital malformations overall in association with exposure in utero (adjusted odds ratio 1.
25 [28] [29]  strengths and limitations of study Our study was population based and included data from four countries, which suggests good external validity of the results. The Scandinavian population based registers and the EFEMERIS database have previously provided robust data on exposure and outcomes. 30 31 The large study cohort with almost 6000 exposed women and their infants ensured sufficient statistical power to detect moderately increased risks of low birth weight, low Apgar score, preterm birth, growth restriction, and congenital malformations and to detect large increased risks of rare events such as stillbirth and neonatal mortality. However, with only 1125 women exposed in the first trimester, the study was not sufficiently powered to study specific congenital malformations. The information on exposure was collected prospectively, minimising the risk of recall bias.
In the studied countries, maternal healthcare is free of charge and widely accepted by the general population. Almost all pregnant women follow the national programmes, and post-exposure prophylaxis with neuraminidase inhibitors was recommended during the influenza epidemic of 2009-10 and was distributed free of charge or reimbursed to all pregnant women in Scandinavia and in France. In Norway (in contrast to the other participating countries) post-exposure prophylaxis with neuraminidase inhibitors was allowed to be dispensed by pharmacists to pregnant women without a prescription by a doctor. The dispensed drugs were, however, reported routinely to the Norwegian Prescription Database, and these women are included in our study. The internal validity of the study should be high, considering the absence of barriers to healthcare and access to post-exposure prophylaxis in the study population.
We excluded all women admitted to hospital because of influenza during pregnancy in the Scandinavian database. We did not have information on hospital admission during pregnancy for the French data. Hence, this much smaller cohort in the study may include sicker women than the larger Scandinavian cohort. However, the point estimates for the various outcomes were similar in both databases. The estimates in the meta-analysis show that the risk estimates changed only marginally when we combined the two sets of data. The rationale for the exclusion of women admitted to hospital in the Scandinavian data was to reduce confounding by indication, as we can assume that admitted women are sicker than women who receive outpatient care or post-exposure prophylaxis, as well as to safeguard the classification of exposure, as in-hospital drug use is not included in the prescribed drug registers at the individual level so there was no possibility to investigate whether admitted women had received neuraminidase inhibitors.
In addition, we did not have information on the indication for the treatment-that is, whether it was prescribed as post-exposure prophylaxis or treatment for influenza. We assume that most the women were either taking the drug for a relatively non-severe illness or as post-exposure prophylaxis, as women admitted to hospital were excluded. As we excluded women admitted to hospital, our study population may have been healthier than those included in the previous studies. This study was based on information from a Scandinavian database of almost 700 000 mother and infant pairs and the smaller French database EFEMERIS with almost 20 000 mother and infant pairs. In a previous study that was based on data from the national birth registers and the prescribed drug registers in the five Nordic countries-Scandinavia (Denmark, Norway, and Sweden), Finland, and Iceland-the results of analyses based on pooled data at the individual level were compared with those from an aggregate meta-analysis.
36 The authors concluded that, for multi-database studies, the results from the two approaches to analysis would be similar. However, when data are sparse the pooled data are superior to a meta-analysis. We used both approaches in this study, optimising the information by pooling individual data from the Scandinavian countries and, as individual data from the French database could not be provided to the study analyst, doing an aggregate meta-analysis based on the results of the logistic and Cox regression analyses in the two databases. We found no association between exposure to neuraminidase inhibitors and congenital malformations, although we did not study malformations in pregnancies ending before gestational week 22. If neuraminidase inhibitors cause severe malformations leading to a miscarriage or termination of pregnancy, our results of no increased risks of malformations may have been biased.
Conclusion and clinical implications In this large multinational register study, we found no increased risk of adverse neonatal outcomes or congenital malformations associated with exposure to neuraminidase inhibitors during pregnancy. Our results support previously reported findings that the use of neuraminidase inhibitors is not associated with increased risks of adverse fetal or neonatal outcomes. Contributors: HK initiated the study and drafted the study aim. TS, ABB, and SG had full access to the data in the study. TS did the analyses in the Scandinavian database and the meta-analysis. ABB did the analyses in the EFEMERIS database. All of the authors contributed to the study design and interpretation of the results. SG and HK drafted the paper. All authors contributed to and approved the manuscript.
In simultaneous PET-MR, attenuation maps are not directly available. Essential for absolute radioactivity quantification, they need to be derived from MR or PET data to correct for gamma photon attenuation by the imaged object. We evaluate a multi-atlas attenuation correction method for brain imaging (MaxProb) on static [ 18 F]FDG PET and, for the first time, on dynamic PET, using the serotoninergic tracer [ 18 F]MPPF. A database of 40 MR/CT image pairs (atlases) was used. The MaxProb method synthesises subject-specific pseudo-CTs by registering each atlas to the target subject space. Atlas CT intensities are then fused via label propagation and majority voting. Here, we compared these pseudo-CTs with the real CTs in a leave-one-out design, contrasting the MaxProb approach.
In recent years, various solutions have been proposed. In the context of brain imaging, these methods can be grouped into three main families: joint emission and attenuation map estimation during the reconstruction process; MR-based segmentation; and methods that create a subject-specific pseudo-CT from a database of images; further described in the following paragraphs. The maximum-likelihood reconstruction of attenuation and activity (MLAA) algorithm, originally proposed by  Nuyts et al (1999)  for PET/CT imaging, falls into the first category. This iterative estimation method alternates the computation of the emission and attenuation map estimates using solely the emission data. However, the additional unknown variables in this optimization problem widen the set of possible solutions, and the algorithm may well converge on local minima leading to inconsistent emission and attenuation maps, leading to crosstalk artefacts.
This is particularly true for osseous tissues that exhibit a large range of densities as shown by  Catana et al (2010). Recently,  Juttukonda et al (2015)  and  Ladefoged et al (2015)  have proposed approaches that attempt to model the CT image intensity from the UTE signal for the bone class, allowing the computation of continuous attenuation coefficients for bone, using constant coefficients only for the remaining tissue classes. While these approaches have produced encouraging results, their accuracy still strongly relies on the exactness of the initial tissue classification. The last family of methods uses a database of image pairs (MR and CT or MR and PET images) to derive a pseudo µ-map. Unlike MR-based segmentation techniques, this process generates continuous attenuation coefficients for the whole volume.
Some approaches utilize the database in a learning step to establish a model linking MR and CT intensities based on local image features and using either a Gaussian mixture regression model  (Johansson et al 2011 , Larsson et al 2013)  or a super-vector regression model  (Navalpakkam et al 2013). This model is then used to derive for each voxel of the subject MRI the corresponding CT intensity. Patch-based techniques use a database of coregistered MR and CT image pairs to predict a subject-specific pseudo-CT by performing an intensity-based nearest neighbour search between patches extracted from the subject MRI and patches extracted from a database  (Torrado-Carvajal et al 2015 , Andreasen et al 2016). Such approaches are promising but have not yet been evaluated exhaustively. Using a multimodality optical flow deformable model,  Schreibmann et al (2010)  propose to create a simulated CT image that matches the patient anatomy by mapping the CT image of a single subject to the patient space.
In other approaches, a single template is built by averaging several subjects from the database registered to a common space  (Montandon and Zaidi 2005 , Malone et al 2011 , Izquierdo-Garcia et al 2014). This single template is then warped into the subject space with a single registration to derive an attenuation map that is subjectspecific to varying degrees. Finally, true multi-atlas approaches have been used in the MRAC context to generate subject-specific pseudo-CTs from a database of MR and CT pairs  (Burgos et al 2015 , 2014a , Sjölund et al 2015 , Mehranian et al 2016). In contrast to single atlas and template methods, true multi-atlas methods register all CT-MR atlas pairs independently, thereby reducing the influence of errors in the individual registrations. To the extent that such errors are uncorrelated, they tend to cancel each other out.
Multi-atlas techniques have been proposed originally for image segmentation problems, in particular for brain segmentation into anatomical regions where it has been shown that they outperform methods that use averaging prior to registration (i. e. template methods) by a large margin  (Heckemann et al 2006). Independent registrations also give the opportunity to better address inter-subject variability by selecting, in the final step, the most relevant information from the database based on local features. Multi-atlas approaches outperform single atlas methods  (Burgos et al 2014a)  and template methods  (Burgos et al 2015)  for the generation of pseudo-CT images. All proposed attenuation map generation methods have only been assessed in the context of static PET acquisition. Recent work  (Ladefoged et al 2016)  has shown that different AC methods perform differently depending on the PET tracer used ([ 18 F]FDG, [ 11 C]PiB and [ 18 F] florbetapir).
Those results suggest that the AC performance may depend on the tracer spatial distribution (variation of contrast) in brain. Research brain PET studies typically use dynamic acquisitions in which tracer spatial distribution changes over time, and no performance data on MR-based AC for dynamic PET imaging have been published so far. In this work we use dynamic PET data to explore this phenomenon. In a recent study  (Merida et al 2015) , we introduced a multi-atlas approach used to synthesise a subject-specific pseudo-CT by registering individual atlases to the target subject space and fusing atlas CT intensities via label propagation and majority voting (MaxProb method). Here, using an improved atlas database with more subjects and higher CT resolution, we provide a complete evaluation of the MaxProb method on quantitative static PET data, and also, for the first time, on dynamic PET data.
Materials 1. 1. 1. Atlas database. Data were available for 40 subjects (13 males, 27 females) (mean age ± SD, 33. 9 ± 13. 2 years; range, 16-63 years), selected as a convenience sample from our research database on the basis of their PET/CT and MR availability. Data used in this study are anonymized images of subjects who had participated in various ethically approved research studies. The anonymization procedure was registered under the number 1134516 by the competent authority (Comité de Protection des Personnes Sud-Est III). Subjects had been informed that their anonymized images could be used for methodological development, and had been given the option to oppose this use of their data. The subjects' MR images were visually reviewed for conspicuous brain abnormalities (none found). Each subject had a T1-weighted MR image and a PET/CT brain scan.
Three-dimensional anatomical T1-weighted sequences (MPRAGE) were acquired on a Siemens Sonata 1. 5 Tesla MR scanner (TE = 3. 93 ms, TR = 1970 ms, flip angle = 15°). The images were reconstructed in a 256 × 256 × 176 matrix with voxel dimensions of 1 × 1 × 1 mm 3. CT images were acquired on a Siemens Biograph mCT PET/CT tomograph at the energy of 80 keV. The images were reconstructed in a 512 × 512 × 149 matrix with a voxel size of 0. 58 × 0. 58 × 1. 5 mm 3. MR images were corrected for field inhomogeneities using SPM12 (Statistical Parametric Mapping 12; Wellcome Trust Centre for Neuroimaging, UCL, London, UK). Each subject's field-bias corrected MR image was aligned with the CT image using the affine registration tool reg_aladin from the NiftyReg software suite, optimizing normalized cross correlation for the image pair (http://cmictig.
cs. ucl. ac. uk/wiki/index. php/NiftyReg (Ourselin et al 2001) ). Coregistered MR images were resampled to their initial resolution using cubic Hermite spline interpolation. Voxel values in CT images quantitatively represent radiodensity in Hounsfield units (HU). We therefore chose CT image as the reference space in order to avoid interpolation of these values. We use the term atlas to refer to the resulting CT and coregistered T1 MRI image pair. Actual CT images were converted to attenuation maps (µ-map) by applying a bilinear transformation  (Carney et al 2006)  followed by Gaussian blurring (FWHM = 4 mm), and resampled to the PET voxel grid. [ 18 F]FDG data were rebinned into a single 10 min frame, whereas [ 18 F]MPPF data were rebinned into 35 time frames (variable length frames, 15 × 20 s, 15 × 120 s, 5 × 300 s) for dynamic reconstruction.
MRAC methods 1. 2. 1. MaxProb method. Similarly to Mérida et al  (2015)  but with the new database, synthetic pseudo-CTs were generated for each subject in a leave-one-out design. Each subject's MR image was used as a target, and the 39 remaining subjects as the atlas database. Pairwise nonrigid registration of each atlas MR image to the target MR image was computed and used for propagating the atlas CT into the target space. The pseudo-CT was generated through voxelwise atlas selection and intensity fusion. The pipeline of the MaxProb method is shown in figure  1 ; a detailed step-by-step description follows. 1. 2. 1. 1. Registration. MR images from the original database of co-registered MR-CT pairs (see section 2. 1. 1) were mapped to each target subject's MR image using affine registration, followed by non-rigid registration (NiftyReg suite: http://cmictig.
cs. ucl. ac. uk/wiki/ index. php/NiftyReg) (Ourselin et al 2001 , Modat et al 2010) , based on cubic-B-spline, with normalized mutual information as similarity measure and a control point spacing of 5 mm. The transformations obtained from the MR non-rigid registration were then applied to the corresponding co-registered CT. This step yielded the registered database (figure 1, Step 1). The example in orange refers to bone; the process classifies all voxels into one of the three classes.
SingleAtlas method. As a point of reference, we developed a simplified method (SingleAtlas), which uses only one atlas (MR and CT pair), randomly selected from the database. The pseudo-CT is built by registering the atlas MRI to the subject space and then warping the CT image (similar to  Schreibmann et al (2010) ). The same registration parameters as for the multi-atlas approach were used. The transformed single CT constitutes the pseudo-CT. We compared the MaxProb to the SingleAtlas method to determine whether the complexity of the multi-atlas approach yielded any accuracy advantage.
2. 1. 2. Quantitative evaluation. Volumes of the various head tissues and non-tissue components with dissimilar attenuation properties, i. e. air, soft tissue, and bone ('tissues', mean ± standard deviation) within the head mask for all real CTs of the database were 180 ± 36 cm 3 for air, 2309 ± 267 cm 3 for soft tissue and 626 ± 95 cm 3 for bone. A box plot of the Jaccard indices computed per tissue class and methods is shown in figure  3. Mean values, standard deviations, and results from the statistical comparisons are summarized in table 1. The mean   Jaccard index obtained with the SingleAtlas method was 46. 36% for air, 66. 78% for bone and 84. 91% for soft tissue. The MaxProb method systematically performed better, with a Jaccard index of around 10 points above the SingleAtlas method for air and bone.
Main results Pseudo-CTs could be generated with acceptable accuracy with MaxProb. SingleAtlas produced larger errors as well as many more outliers than the multi-atlas approach, a finding that we expected because a single image cannot reflect inter-subject variability  (Heckemann et al 2006). In this paper, we used a larger atlas database (n = 40 MRI-CT pairs) than in our previous work (Mérida et al 2015, n = 27 pairs), with more thinly spaced CT slices. The results were very similar. This is important, as it suggests that the multi-atlas method performs similarly across databases. We showed that both regional and local evaluation of the errors is relevant, since large bias localized in regions near bone may become averaged and not detected in a global and even a regional evaluation. This point is important for studies in neuroscience that focus on small brain structures, but also for clinical studies, e.
Remaining errors on pseudo-CT images In the MRAC methods, attenuation of brain structures that have boundaries with air cavities or with bone is difficult to estimate because of the low contrast between air and bone in the MR images. Figure  6  shows that CT values in such regions are particularly biased for the SingleAtlas method, but are well managed in the multi-atlas approach despite a slight local overestimation of around 300 HU. The effect of error on bone was generalized in the SingleAtlas AC method, and these inaccuracies produced both overestimations and underestimations on PET images all around the cortex. The small residual bias observed on the parametric image for MaxProb (figure  6 ) may be partially affected by a lack of information around the neck in the atlas database. A new database with extended field of view is under investigation.
It is to be expected that reporting clinicians will become accustomed to PET-MR artefacts, just as they already are accustomed to interpreting e. g. FLAIR artefacts, or CT artefacts due to bone or metal. For now, areas close to craniotomies may be difficult to interpret, and clinicians should remain aware of the possibility of large sinuses causing local artifacts. In some special cases, e. g. in the case of brain tumours with craniotomies, it may be preferable to plan for an additional low-dose CT scan. The MaxProb method might be further improved by deriving subject-specific bone information from the MR images, for example via UTE sequences  (Roy et al 2014 , Ladefoged et al 2015) , and combining it with the multi-atlas pipeline. It may also be possible to explore the number of atlases selected during the MaxProb fusion process and use this to determine optimal numbers of (similar) atlas pairs in the database and/or selection of atlas pairs.
Conclusion Our findings entail the following points that we consider consequential. 1) Knowledge about the correlation between MR signal intensity and tissue attenuation should be drawn from a multi-subject reference database -using single-subject reference correlation data leads to inferior attenuation maps. 2) Inaccuracies in attenuation map estimates lead to biases that depend on the spatial distribution of the activity, which can be problematic in dynamic PET imaging involving kinetic modelling. 3) Multi-atlas attenuation correction provides highly accurate data that are largely equivalent to data corrected via CT. The method presented here entails little bias in static PET activity estimation or in the BP ND maps generated through compartmental modelling and should therefore be of sufficient quality and robustness for research applications.