dataesr/scientific-paragraphs-categorization huggingface dataset:
The original investigators continued to have control over how their data were analyzed. Newly planned analyses, such as reported in this paper, were only done if collaborators were fully informed and agreed with them. The need for review by an ethical board has been waived. However, collaborators providing individual patient data, signed a declaration that under no circumstance patient information could possibly be linked to the patient identity. From the 17 eligible RCTs on this topic in the literature, 10 trials provided pre-specified raw data from each individual study participant, including patients' characteristics, ventilation parameters, early blood gas values and neonatal outcomes. These data from 3229 patients, born between 1986 and 2004, were stored in a central database. The mean gestational age of these infants was 27.
3 weeks (standard deviation (SD) ±3. 8 weeks) and mean birth weight was 989 grams (SD ±315 grams). External validation of the retrieved models was performed using the PreVILIG database after agreement by all the PreVILIG collaborators. In this dataset, patient characteristics such as gestational age, birth weight, gender, Apgar score at 5 minutes and antenatal steroids were available for all infants. The median age at randomization varied between 0. 3 and 13. 5 hours after birth. Information on mean airway pressure (P aw ) and the fractional inspired oxygen concentration (FiO 2 ) were provided for the first 24 hours and data on ventilator settings during the first 72 hours after randomization. Data on the arterial partial oxygen tension (PaO 2 ) were collected on randomization, whereas partial carbon dioxide tension (PaCO 2 ) values (arterial or capillary) were available for the first 72 hours after randomization.
Clinical data on surfactant use, postnatal age at randomization, and age at extubation; morbidities such as persistent ductus arteriosus, pneumothorax, pulmonary interstitial emphysema and intracranial hemorrhage; and death at 36 weeks PMA as well as the incidence of BPD defined as oxygen dependency at 36 weeks PMA were also collected. In general, the percentage of missing information from the individual patient data was low, less than 10%. Most prediction models used conventional respiratory support in their developmental cohorts and therefore included solely conventional respiratory settings as predictor variables. The external PreVILIG cohort included infants on HFV and on conventional ventilation  [19]. No apparent difference was seen in the outcome estimate BPD or the combined outcome death or BPD in the individual patient data (IPD) analysis by Cools et al.
[19]. Therefore, the IPD of both intervention arms (HFV and conventional ventilation) were included in the analyses in the calculation of the prediction model. For models including predictors of conventional ventilation, only the patients in the IPD assigned to the conventional arm could be used. We assessed the discriminative performance of the included models using data of infants who were randomized to the conventional ventilation arm in a separate analysis and compared the results with the analysis of data from all infants.
Statistical analyses The included prediction models were validated using the reported information (i. e. regression coefficients, score charts or nomograms) by matching the predictors in each model to the variables in the PreVILIG dataset. A direct match was available in the PreVILIG dataset for most variables. When a predictor was not available in PreVILIG, we sought to replace the variable with a proxy variable. When no proxy variable was possible, we randomly substituted (e. g. imputed) the mean value reported in the literature for these predictors  [21]. To prevent over-imputation this procedure was only performed when the missing predictor from the model had a low weight in the equation compared to the other predictors. If none of these methods could be applied, the clinical prediction model had to be excluded and was not tested in the external cohort.
Using these methods, we calculated the probability of developing BPD at 36 weeks PMA and the combined outcome death and BPD at 36 weeks PMA for each individual patient in the PreVILIG dataset. Although not all retrieved models were developed to predict both outcomes, the performance of all models was evaluated for both outcomes in terms of their discrimination and calibration. First, the discriminative performance of the prediction models was quantified by constructing receiver operating characteristic (ROC) curves and calculating the corresponding area under the curves (AUC) with a 95% confidence interval. The ROC curve is commonly used for quantifying the diagnostic value of a test to discriminate between patients with and without the outcome over the entire range of possible cutoffs. The area under the ROC curve can be interpreted as the probability that a patient with the outcome has a higher probability of the outcome than a randomly chosen patient without the outcome  [17].
Second, the calibration of all models was assessed. This describes the extent of agreement between the predicted probability of BPD (or the combined outcome death or BPD) and the observed frequency of these outcomes in defined predicted risk strata. Model calibration was visually assessed by constructing calibration plots and evaluating agreement between predicted and observed probabilities over the whole range of predictions  [17]. As the calibration of a predictive model in an independent data set (external validation set) is commonly influenced by the frequency of the outcome in the validation set, we adjusted the intercept of each model using an offset variable in the validation data to account for prevalence differences between the populations before applying it to the data, such that the mean predicted probability was equal to the observed outcome frequency  [22].
Calibration plots were constructed for the top 5 discriminating prediction models  [23]. In order to determine the impact of the missing values within the PreVILIG database on the performance and accuracy of the prediction models, missing data were imputed by means of multiple imputation using "Multivariate Imputation by Chained Equations" (MICE)  [24]. This procedure is an established method for handling missing values in order to reduce bias and increase statistical power  [21]. Missing values were imputed 10 times for each separate trial, or, when variables were completely missing within a trial the median observed value over all trials was used. Estimates from the resulting 10 validation datasets were combined with Rubin's rule (for calculating AUCs) and with averaging of model predictions (for constructing calibration plots)  [25].
Characteristics of prediction models The models' characteristics (Table  1 ) are presented for derivation studies (i. e. studies developing a novel prediction model) and validation studies (i. e. studies evaluating a single predictor or a known model for outcomes other than BPD). All models show much heterogeneity with respect to the years the data were collected, study design, total numbers of patients and gestational age. Nine of the derivation cohorts included non-ventilated patients in their developmental cohort (50%). Most studies were based on collection of data in a single-center setting. The earlier prediction models calculated their models on the outcome BPD at 28 days of postnatal age, whereas after the millennium all studies aimed for the diagnosis of BPD at the 36 th week PMA. Two models defined BPD according to recently established international criteria  [52, 53].
External validation of the eligible models We were able to perform external validation with the PreVILIG dataset in 19 of the 26 eligible prediction models. One study did not present the actual formula of the derived prediction model. The original investigators were not able to provide these data, and therefore its validation was not possible  [27]. Two authors provided estimated predictor-outcome associations that were not described in the original reports  [39, 40]. One author agreed to re-analyze their data in order to construct separate models for predicting the combined outcome of death and BPD  [40]. Six models could not be validated because variables on either fluid intake, weight loss after the first week of life, or exact duration of high oxygen and positive inspiratory pressure were not available in the PreVILIG dataset and no proxy variable could be imputed  [12, 13, 28, 35, 38, 47].
One study presented three models: a score chart, a dichotomized predictor and a model keeping all continuous variables linear  [54] ; the latter of these models was validated with the PreVILIG dataset  [32]  The method of replacing a missing variable by a proxy was used in 3 prediction models  [40, 45, 46]. The "base excess" values were imputed according to the mean values found in the literature  [55, 56]. Because subject ethnicities were not recorded in the PreVILIG validation dataset, imputation was applied on a per-trial level according to reported percentages of ethnicity. If this information was not available, the local percentage was imputed. For one model, the variable "pulmonary hemorrhage" was removed from the equation, since in the literature a negligible frequency of this complication was found, confirmed both by clinical experience and the low frequency in the original developmental cohort of this model itself  [26].
Discriminative performance The discriminative performance of the models validated with the PreVILIG dataset (Table  4 ) in the complete case analyses (CCA) and multiple imputation analyses (MI) ranged from 0. 50 to 0. 76 for both outcomes. Regarding the outcome BPD, superior discrimination was achieved for multivariable models, with AUC values above 0. 70 (CCA). The model derived by  Ryan et al. in 1996  achieved the best discrimination [AUC 0. 76; 95% confidence interval (CI) 0. 73, 0. 79], and their previous model reported in 1994 performed similarly  [29, 31]. Also the model of Kim et al. showed fair discrimination. These models calculate the prediction on the 7 th  (Ryan 1994 ) and 4 th  (Ryan 1996, Kim)  day after birth, a relatively late stage  [29, 34]. Only two models that had an AUC above 0.
In contrast with predicting the outcome BPD, external validation of the univariable variables gestational age and birth weight showed an AUC ≥ 0. 70 when calculated for the combined outcome death and BPD at 36 weeks PMA, underlining the weight of these two variables for the prediction of that outcome. The range of number of patients with data on the required variables available in the PreVILIG dataset for the different models varied widely from 322 to 2429 patients. This may explain why validation results from CCA and MI sometimes considerably differed. However, multiple imputation generally resulted in a decreased AUC and these differences did not exceed 10% of the original score (Table  4 ). The model derived by Laughon et al. achieved the highest AUC for both outcomes, with CCA and MI  [40].
Calibration The calibration was assessed for the 5 best-discriminating models on both the outcome BPD at 36 weeks PMA and the combined outcome death or BPD at 36 weeks PMA (CCA and MI)  [14, 26, 31, 34, 40]. These plots are presented after adjustment of the intercept of each model in the validation data (Figures  3, 4 , 5, 6 and 7) respectively displaying the outcome BPD (A) and the combined outcome death or BPD at 36 weeks (B)). The dashed line represents the ideal calibration (with intercept 0 and regression coefficient 1). The dotted line represents the calibration performed with complete case analysis (CCA), whereas the dash-dot line represents the multiple imputation analyses (MI). Because the incidence of BPD in the PreVILIG dataset differed from the original derivation cohorts, the calibration plots are presented with an adjusted intercept.
Discussion We present the first systematic review identifying, appraising and externally validating all previously published prediction models on BPD in premature infants. We identified 26 studies published over 30 years. The external validation of 19 prediction models in the large independent PreVILIG dataset showed a broad range of discrimination performance from poor to fair. Furthermore, even with fair discriminative performance, only Figure  2  Methodological quality of the observational cohorts according to the STROBE instrument. Per item in the STROBE instrument, the red colour represents high risk of bias ("No"), the blue colour represents unclear risk of bias ("Unclear"), and the green colour represents low risk of bias ("Yes"). two models showed good calibration  [31, 40]. The implementation of these models in daily clinical and research practice was previously jeopardized by several limitations.
First, identified prediction models were rarely externally validated, but were predominantly evaluated by some type of internal validation. The few external validation studies done were performed in small datasets, rendering published results highly susceptible to sampling variability. Furthermore, almost all studies excluded the cases with missing values or did not specify how these cases were handled during validation  [21]. As a consequence, most identified prediction models had an unclear generalizability  [10]. Our multiple imputation analyses showed that only three models reached an AUC.
One model with good calibration has a limited usefulness because it only estimates the BPD risk on the 4 th day of life and does not account for other forms of mechanical ventilation  [31]. Another model with good calibration could not be fully evaluated because one of its predictors, HFV vs. conventional ventilation, was a randomized variable in the PreVILIG dataset  [40]. Third and most importantly, none of the identified prediction models had undergone a proper implementation phase in clinical practice and research. An impact analysis is needed to quantify whether the use of the prognostic model does improve decisions and treatment and, more importantly, does improve patient outcome  [11]. In summary, we conclude that although the prediction models have a reasonable quality of reporting, there are many gaps in the development, validation and impact stages of the presented models.
Limitations To fully appraise the results of our systematic review, a few important limitations need to be considered. First, of the 26 eligible models, only 19 could be validated in the PreVILIG dataset. Although it is possible that the remaining models have good performance, this remains untested so far, as some predictor variables were unavailable in the PreVILIG study. However, these variables may be difficult to assess in daily clinical practice, jeopardizing their implementation in routine care. For instance, three of the untested models included the concentration of oxygen or positive inspiratory pressure for a prescribed time  [12, 13, 28] , or included weight loss, fluid intake or urine output as predictors  [35, 38, 47]. These variables are not collected easily even in a prospective study and more importantly are not independent of local protocols or habits  [12, 28].
Second, a limitation of the validation using the PreVI-LIG dataset is that this dataset only contains ventilated preterm infants and their available parameters during the first days of life. Today more and more infants are initially managed without invasive ventilation. Although these preterm infants often have decreased need for supplemental oxygen or mechanical ventilation in the first postnatal week, many infants have a pulmonary deterioration in the second postnatal week, with an increased need for supplemental oxygen and respiratory support, and many will eventually develop BPD  [59]. Ideally, the identified prediction models should be validated using a dataset of both ventilated and non-ventilated preterm infants from a recently collected multicenter cohort, defining the outcome BPD according to recent established criteria that include the severity of the diagnosis.
The PreVILIG dataset did not access the severity of BPD, and furthermore no prediction model with extensive ventilator parameters could be validated after the third day of life. However, the strength of the PreVILIG dataset is the large number of included patients, with comparable mean gestational age compared to the best five performing models, in an IPD database containing detailed information on clinical data and respiratory support during the first week after birth. Even when the limitations of this dataset are taken into account, those prediction models that have adequate generalizability should perform similarly in this dataset, as if it were a mixed dataset of both ventilated and non-ventilated infants. To assess the risk of bias due to non-randomly missing values, the calculations were rerun after multiple imputations.
Although these predictors are both established risk factors for BPD, combining the two might not improve the accuracy of the model due to collinearity  [52, 58]. Any future model should report validation analyses, showing both discriminating and calibration performance and handling missing values in the dataset by imputation, rather than exclusion  [10, 21, 65]. If this study reveals a model with sufficient performance, an international consensus conference should be held to determine the utility of this model and, guided by this, review what variablesat any stage after birth -could improve the prediction rule without neglecting the previous model. This method is preferable to developing yet a whole new model in isolation  [22]. Finally, clear impact of using that model should be provided by showing evidence that it appropriately selects candidates for preventive interventions, and future trials investigating new interventions on the important health outcome BPD.
Related literature For general background to 2-aminobenzamide derivatives, see:  Nagaoka et al. (2006) ;  Butsch et al. (2011) ;  Kapoor et al. (2010). For related structures, see:  Yang et al. (2012) ;  Lah et al. (2006). For standard bond lengths, see: Allen (i) Àx þ 1; Ày; Àz þ 1; (ii) Àx; y À 1 2 ; Àz þ 3 2 ; (iii) x; y; z þ 1. Data collection: CrysAlis PRO  (Agilent, 2011) ; cell refinement: CrysAlis PRO; data reduction: CrysAlis PRO; program(s) used to solve structure: SIR2002  (Burla et al. , 2003) ; program(s) used to refine structure: SHELXL97 (Sheldrick, 2008); molecular graphics: ORTEP-3 for Windows  (Farrugia, 2012)  and DIAMOND  (Brandenburg & Berndt, 2001) ; software used to prepare material for publication: WinGX  (Farrugia, 2012). This work was supported by the Unitede Recherche de Chimie de l'Environnement et Moleculaire Structurale, CHEMS, UniversiteConstantine1.
Computing details Data collection: CrysAlis PRO  (Agilent, 2011) ; cell refinement: CrysAlis PRO  (Agilent, 2011) ; data reduction: CrysAlis PRO  (Agilent, 2011) ; program(s) used to solve structure: SIR2002  (Burla et al. , 2003) ; program(s) used to refine structure: SHELXL97 (Sheldrick, 2008); molecular graphics: ORTEP-3 for Windows  (Farrugia, 2012)  and DIAMOND  (Brandenburg & Berndt, 2001) ; software used to prepare material for publication: WinGX  (Farrugia, 2012). where P = (F o 2 + 2F c 2 )/3 (Δ/σ) max = 0. 003 Δρ max = 0. 41 e Å -3 Δρ min = -0. 41 e Å -3.
ENVIRONMENTAL DATA The maximum, minimum and mean daily temperatures and rainfalls were obtained from meteorological stations located at 25 and 50 km from the rivers sections monitored in Douroula and Kadomba, respectively. In addition, daily and nightly Land Surface Temperatures (LSTd and LSTn, respectively) measured from MODIS satellites were used as monthly average of the values in the 1 * 1 km pixels intersecting the sites where traps were set.
STATISTICAL ANALYSES The fly infection rates were analyzed using generalized linear mixed binomial models (Laird and Ware, 1982) using the species, sex, and monthly averages of the maximum, minimum and mean daily temperatures, rainfalls, LSTd and LSTn as fixed effects and the trap position as a random effect. The best model was considered as the one with the lowest corrected Akaike information criterion (AICc)  (Hurvich and Tsai, 1995; Burnham and Anderson, 2002). The R software (R Core Team, 2013) was used for statistical analysis, together with the lme4 package for the linear mixedeffect model  (Bates et al. , 2011)  and the MuMin package for the implementation of the AICc  (Burnham and Anderson, 2002).
ACKNOWLEDGMENTS This work benefited from international collaboration through the  Centre International de Recherche-Développement sur l'Elevage en Zone Subhumide (CIRDES)  and the  Centre de. Coopération Internationale en Recherche Agronomique pour le Développement (CIRAD). It was carried out with the support of the  Fragfly Wellcome Trust  (Project  075824 ; http:// wt-fragfly. cirad. fr/). We are indebted to the director general of CIRDES,  Professor Abdoulaye Gouro  for the provision of excellent working conditions, and to  Adama Sana ,  Bila Cene. Félix Sanou  and  Lansina Sanogo  for assistance during the field studies. Meteorological data were kindly provided by weather stations [Agence pour la  Sécurité de la Navigation Aérienne en Afrique et à Madagascar (ASECNA)] at Dédougou  and  Bobo-Dioulasso  and MODIS processed data by the  Department of Zoology of the University of Oxford.
Besides, it is important to calibrate the MLVA method against PFGE on the one hand and MLST on the other hand. Indeed, PFGE is the standard for epidemiological investigations, whereas MLST is well established as a population biology tool and provides a standard operational definition of clones as clonal complexes (CCs)  (12, 36). Only one of the five proposed MLVA schemes was compared with PFGE data obtained by following the current standard protocol of dual restriction enzyme (ApaI and AscI) use  (29) , while a comparison with MLST has been performed in only one study so far  (27). The added value of MLVA relative to PFGE and MLST thus clearly remains to be precisely defined. The aims of our study were (i) to identify novel VNTR loci and evaluate them, as well as all previously described loci, for typeability of isolates representative of a broad range of genotypes of lineages I and II, including all major clones defined by MLST; (ii) to evaluate the ability of MLVA to identify MLST-defined clonal groups; and (iii) to evaluate and compare to PFGE, the added value of MLVA in terms of strain discrimination within clonal groups of particular interest.
PCR serogrouping and MLST. PCR serogrouping and MLST genotyping were carried out as previously described  (12, 17). Primer optimization and identification of novel MLVA loci. First, we included all 15 of the VNTR loci described previously  (25) (26) (27) (28) (29). The VNTR loci common to more than one of the above studies were amplified with all of the different PCR primer sets defined previously (Table  1 ). Second, to identify additional VNTR loci, the tandem repeat site http: //minisatellites. u-psud. fr (39) , which uses the software Tandem Repeat Finder  (40) , was used for the genomes of strains EGDe and F2365, with a minimal unit length of 6 bp, a minimal copy number of two repeats, and an 80% sequence match. Three novel repeat regions (Lis-TR357, Lis-TR495, and Lis-TR1869) were identified.
The same PCR cycle was used for M1 and M2 multiplex PCRs: 95°C for 15 min, followed by 35 cycles of 94°C for 30 s, 56°C for 90 s, and 72°C for 90 s, with a final extension at 72°C for 10 min. PCR amplicon sequence verification. The purified PCR products of single PCRs performed with unlabeled primers were sequenced to control the molecular origin of size variation. The DNA sequence for every distinct allele of all VNTR loci was determined. Nucleotide sequence data were edited and compared with BioNumerics version 6. 6 (Applied Maths, Sint-Martens Latem, Belgium) and Multalin (http://multalin. toulouse. inra. fr/multalin/). Stability of MLVA alleles. The L. monocytogenes EGDe strain was passaged on Columbia agar medium 65 times over a period of 4 months. At each passage, a sweep was taken and streaked onto the next Columbia agar medium tube.