dataesr/scientific-paragraphs-categorization huggingface dataset:
No restrictions regarding language or publication type (articles or reviews) were applied in this phase. This search strategy led to 201 PubMed papers, 271 Scopus papers, and 256 Web of Science papers. The titles and abstracts of all references were retrieved after removing duplicates. They were independently screened in parallel by 2 members of the project team with respect to the inclusion/exclusion criteria. Articles that did not meet the inclusion criteria (see below) were excluded from further analysis, although a few are cited in the discussion. The 2 reviewers compared findings for consistency with the aim to highlight any discrepancies and define an agreed final set of papers to be analyzed. All the abstracts were reviewed and classified according to different categories (occupational HBM data on phthalate exposure, analytical methods, toxicological studies, not on the topic, not in English, occupational exposure study without HBM, HBM in the general population, review, HBM with effect biomarkers), and the final set of studies was decided (Fig.
Studied phthalates in HBM studies on occupational exposure Table  2  presents the HBM studies on occupational phthalate exposure. Most of the studies present urinary data on "old" phthalates, which are no longer used in Europe or remain in limited use. DEHP was analyzed in 19 out of 22 studies (86%), of which only 7 deal only with DEHP exposure. Other old phthalates studied were DMP (4 studies), DEP (10), DBP (11), DiBP (3), BBzP (4), and DOP (2). Occupational exposure to "newer" phthalates such as DiNP, DiDP, and DPHP was investigated much less frequently. Five studies were identified in which DiNP exposure was evaluated  (Hines et al. , 2012; Koch et al. , 2012; Kolena et al. , 2014; Petrovicova et al. , 2014; Pilka et al. , 2015) , and in one of them, workers were also co-exposed to DiDP or DPHP  (Koch et al.
North American studies In the 4 North American HBM papers  (Cavallari et al. , 2015; Hines et al. , 2009 Hines et al. , , 2012;; Kwapniewski et al. , 2008) , most of the studied biomarkers corresponded to old phthalates (DEP, DBP, DEHP, DiBP, BBzP), except for the study of  Hines et al. (2012) (DiNP). In the earlier study of  Hines et al. (2009) , the sample size exceeded 100 individuals, which provides a good overview of the exposure in different sectors of the studied manufacturing companies. No control group was included in these studies, although the results were compared to the data from the general US population. Hines et al. (2009 Hines et al. ( , 2012) )  collected mid-and end-shift urine samples in a single work shift on any day of the working week. Kwapniewski et al. (2008)  and  Cavallari et al.
Acknowledgements The  HBM4EU  project has received funding from the  European Union 's  Horizon 2020 research and innovation program  under grant agreement no. 733032\  and received co-funding from the author's organizations. We would like to thank  Lise Sainson , documentalist at SpFrance. In addition, the authors of the HBM4EU deliverable D 4. 2 of  HBM4EU  project (Schoeters et al. , 2017) are acknowledged for the background information that they provided on the relevant phthalates and their use.
Background: Chronic kidney disease (CKD) frequently causes death in older cats; its early detection is challenging. Objectives: To build a sensitive and specific model for early prediction of CKD in cats using artificial neural network (ANN) techniques applied to routine health screening data. Animals: Data from 218 healthy cats ≥7 years of age screened at the Royal Veterinary College (RVC) were used for model building. Performance was tested using data from 3546 cats in the Banfield Pet Hospital records and an additional 60 RCV catsall initially without a CKD diagnosis. Methods: Artificial neural network (ANN) modeling used a multilayer feed-forward neural network incorporating a back-propagation algorithm. Clinical variables from single cat visits were selected using factorial discriminant analysis.
| Datasets sourced from clinical studies Data for the development of the diagnostic algorithm were extracted from the database of the Feline Kidney Research Clinic at the Royal Veterinary College (RVC database), which contains the data of cats recruited to studies conducted there since 1995. These were cats that owners had believed to be healthy and had been recruited to be screened for kidney disease and followed longitudinally at 2 clinics in central London (UK), the Beaumont Sainsbury Animals' Hospital (BSAH), Royal Veterinary College, Camden, and the People's Dispensary for Sick Animals (PDSA), Bow. A first dataset (RVC1) was extracted from the RVC database from cats added between 1995 and November 2013 and used for prediction algorithm development. A second dataset (RVC2) later was extracted for validation purposes and consisted of (1) healthy cats added to the database between November 2013 and April 2016 and (2) cats that were in the database before November 2013 but were not eligible for RCV1 because they only reached 18 months of followup after November 2013.
[16] [17] [18] [19]  To be included in either the RVC1 or RVC2 dataset, cats had to be ≥7 years of age and considered healthy based on history, physical examination, blood biochemistry and urine screenings. Where available, blood pressure measurements (standardized Doppler method, Parks Electronic Doppler Model 811B, Perimed UK, Bury St Edmunds, UK)  20  were included in algorithm development but were not used to determine health status. These datasets encompassed a large set of numerical and nonnumerical variables per time point that described the cat's environment,  21  signalment, clinical examination findings, vaccination history, packed cell volume, plasma biochemistry, urinalysis, and biomarkers such as parathyroid hormone (PTH) and fibroblast growth factor 23 (FGF-23). For cats that remained nonazotemic to be included in the datasets, at least 1 follow-up visit had to have occurred ≥540 days (18 months) after the initial screen at which the cat was examined, and blood and urine samples must have been collected to assess renal function.
The datasets also included data from any follow-up visits between the initial screen and 540 days. Similarly, the dataset contained all data from multiple visits by cats documented as azotemic (plasma creatinine concentration ≥ 2 mg/dL) after the initial screen. All cats were categorized as either becoming azotemic after the initial health screen (cases) or remaining nonazotemic over at least 18 months (controls). A single group of veterinary-qualified staff from the RVC reviewed the records from baseline to agree on the initial classification of cats as healthy and from follow-up visits to verify the diagnosis of CKD.
| Dataset from a large group of primary care clinics To test the model performance on data collected by primary care practitioners in a clinical service setting, a subset of domestic short, medium and long-haired cats visiting Banfield Pet Hospitals (Vancouver, Washington) between January 1995 and June 2016 was identified. Data extracted from 6. 5 million electronic medical records included reproductive status, breed, age and laboratory results at each visit. From those records, 24 497 CKD cats were identified by either a formal recorded diagnosis of CKD or by at least 2 CKD-suggestive data points from the following list: plasma creatinine concentration above normal, urine specific gravity (USG) below normal, and "CKD," "azotemic," "Royal Canin Veterinary diet Renal," or "Hill's prescription diet k/d" in the medical notes.
A first set of filters was applied to retain CKD cats presented in a Banfield Hospital as healthy before the onset of CKD and for which there were at least 2 visits with a measurement of plasma creatinine concentration as well as to remove outliers, yielding 1855 CKD cats. A second set of filtering criteria then was applied to ensure that data were suitable for modeling, including age between 7 and 22 years, and at least 2 visits at which plasma creatinine and blood urea nitrogen (BUN) concentration and USG were measured, yielding 1510 CKD cats. Controls were cats that had not been diagnosed with CKD and had a further 2 years of visits beyond the last data point provided to the model, during which they remained free of CKD. They were not specifically healthy cats and could have been diagnosed with any other illness.
| RVC1 and RVC2 datasets Owners were asked to withhold food for 8 hours before blood samples by jugular venipuncture were collected into lithium heparin and plain tubes. Heparinized plasma was used for immediate biochemical analysis (Idexx Laboratories, Wetherby, West Yorkshire, UK). A urine sample collected by cystocentesis was required for study eligibility. Urinalysis included measurement of specific gravity, pH (HI 9224 pH meter, Hanna Instruments, Leighton Buzzard, UK), dipstick chemistry analysis (Multistix Urine Chemistry Reagent Strips, Bayer Diagnostics, Newbury, Berks, UK) and microscopic examination of sediment. Residual samples were centrifuged (Mistral 3000, Sanyo-Gallenkamp, Leics, UK) at 4 C for 10 minutes, and stored at -80 C for later batched measurement of urine protein-to-creatinine ratio and urine albumin-tocreatinine ratio.
Phase Each phase of the model building represented the application of a given dataset or combination of datasets to either build or validate the model F I G U R E 1 Design of datasets for time-delineated submodels. The chart shows the flow of data into time-delineated submodels with respect to a single visit. A cat had a separate M0 status for each of its visit, which were analyzed independently. A given visit was associated with a chronic kidney disease (CKD) status (positive or negative) for each of the submodels M0, M3, M6, M12 or M12+, depending on the time interval in months between the visit and the date when the cat was diagnosed or not with CKD. Cats diagnosed with CKD at or before the date of any visit were included as positive for the associated M0 submodel but excluded from other submodels, because unlike M0 these were designed to predict a future not a present status.
*M0 status at the initial visit was negative for all cats because this was an eligibility criterion. † Once a cat was positive at any of the visits it was automatically recorded as positive for all subsequent visits and was no longer followed up that is, it no longer provided input for submodels other than M0 visit. For each visit, a dataset was built a posteriori by coupling disease status at the time of the visit, the set of variables measured from the samples taken, and the visit date. Each dataset was used as input to create 6 submodels that could use measures from a single visit to predict the current (submodel M0) or future occurrence of CKD within 3, 6, 9, 12, or >12 months (submodels M0, M3, M6, M9, M12, and M12+, respectively; Figure  1 ). The CKD status of a cat at the time of a visit was termed the M0 status.
| Initial selection of variables The raw dataset was cleaned from 116 to 16 variables by excluding 55 with >40% missing data, all remaining 36 qualitative variables because of their minimal relevance to CKD or paucity of abnormal findings, and 9 blood variables that were not consistently present with the same other variables to constitute complete sets of measures. Variables remaining were: age, creatinine, USG, chloride, total plasma protein, phosphate, total plasma calcium, albumin, globulin, urea, alanine transaminase, alkaline phosphatase, bilirubin, cholesterol, sodium, and potassium. Factor discriminant analysis (FDA) with a threshold fixed to j0. 5j was used for variable selection from these 16 variables. 22, 23 It was applied separately on each submodel dataset to ensure that the best predictors for each time range were retained.
" During the training step, the internal parameters of the MLP were tuned simultaneously in a full factorial design that tested all combinations for each submodel. These parameters were the number of hidden layers, the number of neurons in the hidden layer and the decay. There was 1 hidden layer, neuron numbers were tested from 2 to 30, and decay was set to vary from 0. 001 to 0. 1. Receiver operator curves (ROCs) were generated for the validation datasets by plotting the true positive rate, or sensitivity, against the false positive rate (equal to 1-specificity) across various thresholds. Optimal parameter values were selected based on the area under the curve (AUC) of the ROCs as the measure of model accuracy. Because the prediction was based on a single visit, the final calculation applied the best submodels on all the initial data based on one visit from each cat.
| Composition of datasets Data from 218 RVC cats (RVC1 dataset) were used to design and build the initial model in phase 1, and data from 60 RVC2 cats and 3486 Banfield cats were used to validate the model in phase 2 as an independent dataset (Table  2 ). Visits were excluded due to missing data (701 from RCV1 dataset) and for cats <7 years of age (31 from RCV1; 9835 from Banfield), leaving a total of 10 576 visits for analysis-672, 60 and 9844 from RCV1, RCV2, and Banfield datasets, respectively (Tables  2  and 3 ). The baseline characteristics of cats are summarized in Table  4. Most cats were neutered, the overall proportion of female cats ranged from 51% to 56%, mean age ranged from 11. 1 years in the Banfield data set to 13. 2 years in the RCV1 dataset. Over the study period, 76% of cats in the RVC1 dataset and 57% of Banfield cats remained nonazotemic, corresponding to an overall prevalence of visits with a positive CKD for the model building of 24% and 43%, respectively.
Of the cats included in phase 1 of the modeling (RVC1 dataset), 11. 5% showed azotemia within 6 months and 15% at 12 months. In phase 3 of the modeling, in which the same model design was used to regenerate the model by fresh training and validation using a combined dataset of RVC1 plus RCV2 plus Banfield, 11. 4% of included cats showed azotemia within 6 months and 24% at 12 months. Table  3  shows the total number of visits made by cats with and without CKD that contributed to the modeling (negative controls and positive cases, respectively). Table  3  also shows the number of visits with positive or negative status that formed the input for each submodel. The prevalence of CKD for each submodel is presented in Supporting Information Table  S1.
| Input variables selection For each of the time-delineated submodels (M0-M12+) the same 3 variables had a discriminant weight ≥ j0. 5j on FDA: creatinine, USG and urea (Figure  2 ); these were retained as input variables for the predictive model. Using the final dataset for phase 3 (RVC1 plus Banfield plus RVC2), the correlation between creatinine and USG as well as the correlation between urea and urine SG varied between -0. 2 and -0. 3 (Figure  2 ). The correlation between creatinine and urea was approximately 0. 54. These correlations were low enough to exclude potential modeling problems associated with covariation of predictors.
| Model performances The performance of the model in each phase of development is summarized in Tables  5  and 6. The ROCs illustrating the accuracy, sensitivity and specificity of submodel M12 are shown in Figure  3. When the initial model from phase I (RVC1 dataset) was established, specificity and sensitivity were optimized (strategy 1). The submodels had specificities ranging from 0. 72 to 0. 93 and sensitivities from 0. 83 to 0. 93 (Table  5 ). The negative predictive values (NPVs) were close to 1 and the PPVs were approximately 70%. A second strategy was tested optimizing specificity and, in doing so PPV, but at the cost of decreasing sensitivity and NPV (Table  6 ). In phase 2, applying the Banfield and RCV2 datasets to the initial model was associated with high accuracy and sensitivity but a decrease in specificity and PPV for both strategies but especially strategy 1, which had an accuracy of 0.
T A B L E 2 Numbers of cats and visits used to create and validate the model When the model was updated in phase 3 using the Banfield, RVC1 and RVC2 datasets, the sensitivity for strategy 1 was generally slightly lower than for phases 1 and 2 and the NPV was similar (Table  5 ). Specificity and PPV were intermediate. Strategy 2 in phase 3 improved specificity to levels similar to those for strategy 2 in phase I and PPV was higher than for phase 2 (Table  6 ). Sensitivity was decreased compared with other phases and a modest decrease in NPV was noted. The accuracy of the model in all phases was close to 90% with the exception of submodel M12+, for which accuracy was closer to 80% (Tables  5  and 6 ). In the final model, the rate of incoherence between submodels was 3.
| DISCUSSION A model was developed that identifies cats ≥7 years of age that are at risk of developing azotemic CKD within 12 months, based on laboratory variables very familiar to veterinarians in general practice. The work is distinct from classical CKD prediction research in cats  10, 19, 21, 27  owing to its use of ANN and the large size of the population for model validation and refinement. Artificial neural network modeling is a powerful tool that can harness the value of large and complex datasets and that, in the era of "big data," is of increasing interest in medical diagnostics (imaging and histopathology in particular) and prognostics. 28, 29 Disease predictions from ANN models are not limited by an understanding of the associated pathophysiology because they are based purely on mathematical relationships.
The size and relevance of a dataset from general practice were considered to outweigh any disadvantages of this approach. Although the model's accuracy was lower for longer predictive intervals, it remained high when both RVC and Banfield data were used. A specific strength of the Banfield dataset was the inclusion of control cats with medical conditions other than CKD. Any mass screening tool for clinical practice must have predictive value in client-owned cats presented to veterinary practice for different reasons and with a range of preexisting conditions. The prevalence of CKD in the Banfield population was considerably higher than in the RCV1 dataset despite the Banfield cats being generally younger, but it is not outside of reported ranges. 32, 33 It is plausible that CKD is more likely to be detected if cats are being monitored for plasma creatinine and BUN concentrations and USG simultaneously, which was required for our model.
Furthermore, the ability of the RVC model to predict CKD in the Banfield cat population supports its relevance. Of the 16 variables in the cleaned dataset, plasma creatinine and BUN concentrations and USG were clearly the most appropriate input variables by FDA. These are classical markers of renal disease, routinely used in clinical practice to diagnose CKD and are easy to measure from readily available samples. Additional biomarkers, including GFR,  9  serum symmetric dimethylarginine (SDMA) concentration  27, 34  and the phosphaturic hormone FGF-23  10  potentially could improve predictions, but were not evaluated here. 35 Although GFR is recognized as the gold standard measure for the early detection of CKD, it cannot be used for screening senior cats. 9, 36 In a small retrospective study in colony cats, SDMA was shown to be a sensitive and specific biomarker for early diagnosis of CKD up to 4 years in advance of abnormal serum creatinine concentration suggesting impaired renal function.
Modeling was conducted in 3 phases. Phase 1 (2014) was the initial development of the model and its predictive algorithms using the RVC1 dataset. During this phase, the model was designed, the input variables were selected and a first run of training and validation of the model was undertaken. The performance of the initial model was tested in phase 2 (2016) using the Banfield and RVC2 datasets as new, independent data. Phase 3 (2016) consisted of a new cycle of training and validation keeping the same input factors (creatinine,.
identified at least 8 of 10 positive cats that would develop azotemia within 12 months. The decreased sensitivity meant that only 4 of 10 cats at risk would be detected, and the decrease in NPV to 79% meant that 2 in 10 cats predicted to be negative would be false negatives. The choice of strategy for setting the decision threshold should be guided by the purpose of the test and the population characteristics of the cats being screened. It may be more important for a screening test that predicts the future development of a disease to favor the detection of potential positives over false negatives, especially where the recommended action for positive individuals is not harmful, and in the case of more frequent health checks, is relatively inexpensive. Phase 1 of the modeling process designed the model using well characterized cats from a clinical research database (RVC1 dataset).
Despite the modest size of the dataset (218 cats), the model developed from it was robust, retaining high accuracy when presented with Banfield and RVC2 data from 3546 cats in phase 2. Artificial neural network modeling is very much dependent on the dataset on which it has been trained. The decrease in sensitivity observed during phase 2 therefore was expected. It might have been because of the difference I G U R E 3 Legend on next coloumn. F I G U R E 3 Receiver operator curve (ROC) curves of the submodel M12 for the 3 modeling phases. M12 is the submodel for predicting the development of CKD in a time frame of 12 months. A, Phase 1 consisted of the design of the model from the RCV1 dataset, including a first run of training and validation. B, In phase 2 the model was tested using the Banfield plus RVC2 datasets as independent data.
issue can be overcome by continuously training the model on new datasets collected from the field. Only visits without missing data were used to ensure the robustness of the model. It could be updated using statistical techniques to generate missing data, in order to assess its predictive strength when data values exist for only 1 or 2 of the selected variables. The clinical utility of the model also could be improved if sufficient data was available to extend the prediction time frame beyond 18 months. The rate of incoherence between submodels was 3. 2%. This rate is very low and inherent to the use of independent submodels. Moreover, when a negative prediction appeared to contradict an earlier positive prediction, the variables were very close to the thresholds of the submodels, indicating that those individuals were at the margin of being reported as positive.
Their model was different because it relied upon changes over time in the plasma creatinine and BUN concentrations and USG. Our model was designed to study any new case on the basis of a single visit, without a medical history, making it practical for a veterinarian who does not have easy access to past biochemistry test results. The previous model 30 also included age, which was not retained in our model on the basis of the FDA results, probably because most of the healthy cats screened by the RVC were ≥9 years of age. Both models used large datasets from clinical practice, but we have demonstrated that a model can also be created from a much smaller dataset of wellcharacterized cats from clinical studies. The previous model gave predictions of CKD up to 2 years before diagnosis.