dataesr/scientific-paragraphs-categorization huggingface dataset:
| R E SU LT S For this retrospective analysis, a total of N = 35 422 biometric measurements from a cataractous population were transferred to our institute in an anonymised fashion (anonymised at the source). Measurements were carried out at Augenklinik Castrop-Rauxel (N = 24 533) and Department of Ophthalmology and Optometry, Johannes Kepler University Linz (N = 10 889). Out of the total of N = 35 422 biometric measurements, N = 21 108 measurements (21 108 eyes of 21 108 patients; 11 822 female and 9284 male patients; mean age 68. 89 ± 12. 21 years) were finally included after discarding measurements with incomplete data, duplicate measurements of eyes, or insufficient quality check of the biometer. In cases where measurements of both eyes were available, one eye per patient was randomly selected and included in our data set.
As a simplification, especially when dealing with linear Gaussian optics, any asphericity of the IOL surfaces is not taken into account and we restrict the model to the curvatures of both surfaces together with the central thickness and the optic material refractive index instead of using the power of the IOL. Calculating the eye using a thick lens setup is not only a domain of numerical raytracing  (Gatinel et al. , 2021; Langenbucher, Hoffmann, et al. , 2022) , even though raytracing enables consideration of the full shape data including asphericity or prediction of the effect of decentration or tilt of refractive surfaces or the effect of an aperture in the optical pathway. In the current study, we used a large data set with biometric measurements derived from a cataractous population to perform a Monte-Carlo simulation.
As shown in Figure  4 , the results of this prediction are not perfect, and by including mixed terms (not shown in the Results section) the prediction performance could be significantly improved, but at the cost of the simplicity. We therefore decided to provide the simple linear model instead of the more complex mixed model, in order to enable clinicians to implement this model using standard software tools such as EXCEL. There are however some limitations of our study, mostly due to the assumptions made for the Monte-Carlo simulation: firstly, we restricted the modelling to linear Gaussian optics (to the paraxial optical space). With linear optics we can deal with simple calculation formulae based on our pseudophakic model eye with 5 refractive spherical surfaces, but we ignore the exact shape (e.
Introduction Despite technological advancements in recent years, glenoid component loosening remains a common complication after anatomical total shoulder arthroplasty (ATSA) and is one of the main causes of revision surgery. Increasing emphasis is placed on the prevention of glenoid component failure. Previous studies have successfully predicted range of motion, patient-reported outcomes and short-term complications after ATSA using machine learning methods, but an accurate predictive model for (glenoid component) revision is currently lacking. This study aims to use a large international database to accurately predict aseptic loosening of the glenoid component after ATSA using machine learning algorithms. Methods and analysis For this multicentre, retrospective study, individual patient data will be compiled from previously published studies reporting revision of ATSA.
A systematic literature search will be performed in Medline (PubMed) identifying all studies reporting outcomes of ATSA. Authors will be contacted and invited to participate in the Machine Learning Consortium by sharing their anonymised databases. All databases reporting revisions after ATSA will be included, and individual patients with a follow-up less than 2 years or a fracture as the indication for ATSA will be excluded. First, features (predictive variables) will be identified using a random forest feature selection. The resulting features from the compiled database will be used to train various machine learning algorithms (stochastic gradient boosting, random forest, support vector machine, neural network and elastic-net penalised logistic regression). The developed and validated algorithms will be evaluated across discrimination (cstatistic), calibration, the Brier score and the decision curve analysis.
STRENGTHS AND LIMITATIONS OF THIS STUDY ⇒ A large international database will be collected, which increases accuracy, validity and external applicability. ⇒ A clinical prediction model using machine learning algorithms will be developed to estimate the probability of aseptic loosening of the glenoid after anatomical total shoulder arthroplasty. ⇒ An open-access prediction tool based on the bestperforming algorithm will be made available online that can guide medical professionals in personalised treatment decision-making. ⇒ The study is dependent on data provided by third parties, which is a potential source of bias. ⇒ Input variables will be selected and categorised based on completeness and uniformity across data sources, potentially decreasing the amount of detail in the study. Open access risk factors of glenoid component loosening identified patient, treatment and prosthesis characteristics related to glenoid component loosening.
Machine learning is most effective with large amounts of data and is very dependent on the amount of detail. Furthermore, the algorithm needs to be widely applicable; a varied and international database provides the highest external validity. To our knowledge, there are no studies predicting the long-term complications such as aseptic loosening of the glenoid component using advanced machine learning techniques. Furthermore, previous machine learning studies are limited in accuracy and validity due to the sample size and homogeneity. Therefore, this study aims to develop a clinical prediction model for aseptic loosening of the glenoid component using machine learning algorithms trained on a large international database using clustered data. The large combined dataset is less prone to overfitting, and allows direct validation of models across a range of populations and settings, thereby increasing generalisability.
METHODS AND ANALYSIS Data collection For this multicentre, retrospective study, individual patient data (IPD) will be collected from previously published studies reporting failure and revision of ATSA. A systematic literature search will be performed in Medline (PubMed) identifying all studies that report a cohort of ATSA including revision as an outcome, published between January 2000 and June 2023. The limit was set at January 2000 to increase the likelihood of the dataset that was used for the study still being available. The minimum required data retention period varies between countries but is generally 20 years or less. The full search strategy is available in online supplemental appendix 1. All original studies reporting revision or failure rates after primary ATSA will be included.
Reviews and letters to the editor will be excluded, as well as studies published in languages other than English, Italian, Dutch and French. Authors will be requested to share the anonymised databases used for the identified studies. Only de-identified databases used for previous studies are included; authors are not required to gather additional data or access patient files. After sharing their data, the authors will be included in the Machine Learning Consortium. Inclusion criteria for individual patients within the provided databases are a minimum age of 18 years and a minimum follow-up of 2 years. Patients who underwent ATSA with a fracture as the indication or patients who underwent concomitant procedures such as a cuff repair, tendon transfer or bone graft will be excluded. The aim is to combine the IPD from previously published studies to create a large international cohort which can be used to train a machine learning algorithm to predict aseptic loosening of the glenoid component after ATSA.
Open access Data curation and missing data Completeness across data sources will be assessed for each variable in the compiled multicentre database, and variables with sufficient completeness (>70% complete) will be selected as input for the machine learning algorithms. Variables with >30% missing data will be excluded. For the remaining variables, missing data will be completed by imputation using multivariate imputation by chained equations. 19  Uniformity in reporting will be assessed for each variable. If possible, variables will be adjusted or categorised to ensure uniform reporting. In case uniformity of the reported variable across data sources cannot be achieved without guaranteeing correctness, the variable will be excluded. Each dataset will be split into training (80%) and test (20%) subsets, stratified by outcome.
Variable selection The primary outcome is a revision of the glenoid component for aseptic glenoid loosening. The input variables for both methods are dependent on the uniformity and completeness of the gathered data but will include demographics (eg, age, sex and ethnicity), patient-specific factors (eg, preoperative BMI, comorbidity, smoking, dominance, previous surgery), disease-specific factors (eg, affected side, indication, Walch classification, fatty infiltration of cuff muscles) and surgical characteristics (eg, corrective reaming, component design and type, component materials, cementing and sizes). Before training the machine learning models, relevant variables will be selected using random forest algorithms with recursive selection. 21  At least 10 events for each predictor variable will be included in the model, adhering to the rule of thumb in predictive models of binary variables.
Development of prediction models Different machine learning models result in varying performance metrics based on the type of input data (continuous, categorical, dichotomous). Due to the variation in type of input variables in the dataset, several different machine learning techniques will be used and compared based on model performance. The following machine learning algorithms were chosen for modelling based on prior research  [23] [24] [25] [26] [27]  : stochastic gradient boosting, random forest, support vector machine, neural network and elastic-net penalised logistic regression. The algorithms will be trained on the training dataset with 10-fold cross-validation repeated three times. Cross-validation means dividing data into a selected number of groups, also called folds. First, the data will be divided into 10 equally sized folds.
Then, the algorithms will be trained on 9 of the 10 folds (90% of the training data) and tested on the remaining fold (10% of the training data). Results will be averaged across all repetitions of this sequence. Machine learning algorithms will be developed using Python (The Python Software Foundation, Fredericksburg, USA). Hyperparameter tuning will be performed as recommended in the Python libraries. The statistician who performs the machine learning analysis will be blinded to the origin of the data, but the anonymised data source will be available to be included as a potential confounding factor.
The TPR is the proportion of true positive outcomes over the number of observed positive outcomes. The precision recall curve is a plot of the PPV versus the TPR. A calibration plot plots the estimated versus the observed probabilities for the primary outcome. A perfect calibration plot has an intercept of 0 (<0 reflects overestimation and >0 reflects underestimation of the probability of the outcome) and a slope of 1 (model is performing similarly in training and test datasets). 20 23 28  The null-model Brier score, which equals the probability of glenoid revision in the dataset, will be used to benchmark the algorithm's Brier score. A Brier score lower than the null-model Brier score indicates superior performance of the prediction model to this null benchmark. Perfect prediction would have a Brier score of 0, whereas a Brier score of 1 would indicate the poorest possible prediction.
28  In addition, the decision curve analysis will be performed and visualised to investigate the net benefit (weighted average of true positives and false positives) of the conducted algorithms over the range of risk thresholds for clinical decision-making. 23 28 29  The net benefit is a weighted average of true positives and false positives, formula=sensitivity×prevalence-(1specificity)×(1-prevalence)×(odds at the threshold probability). The decision curve of the model will be compared with decision curves of treating everyone as being at risk and treating no one as being at risk. 23  Due to the large heterogeneity of the compiled dataset from different international sources and the internal Open access validation of the prediction models, the generalisability of the model can be intrinsically confirmed using the above-mentioned performance tests.
32  Creating an online tool for aseptic loosening of the glenoid component after ATSA can help guide surgeons in selecting patients who will most benefit from this treatment and considering alternatives in cases of high-risk estimates. The strength of this project is the large amount of data that will be gathered from authors participating in the Machine Learning Consortium, aiming to include a minimum of 7500 patients in total. Using a large, heterogeneous international database for development of the algorithm and prediction tool will result in high external validity and may improve applicability worldwide. 15  However, most machine learning techniques require a larger sample size to achieve an accurate prediction compared with traditional regression models. The minimum events per variable are not clearly defined and differ per technique.
Furthermore, in gathering data retrospectively from various sources, the study is subject to variances in the included variables. Low completeness and large variability of reporting may introduce bias. However, only variables that are consistently reported in multiple data sources will be included in the final analysis; variables will be categorised to increase uniformity and missing data will be imputed where possible. The exclusion and categorisation of variables will have to be balanced with the amount of detail in the final analysis. Furthermore, the accuracy of data collection is dependent on third parties providing the data; the method of data collection cannot be verified for all sources. However, the data source will be considered as a confounder. Furthermore, the variety in data sources will increase the external applicability of the algorithm.
Participants The group of participants consisted of 31 people, 23 women and 8 men, aged from 19 to 60 with a median age of 42, right handed. Participants were volunteers. All participants signed a consent of participation (Supplementary data 1), after being informed (Supplementary data 2) and filled a form specifying 1) their age and gender, 2) if they were wearing glasses or contact lenses (participants with glasses were asked to remove them during the test and we checked that wearing contact lenses didn't affect the data acquisition), 3) if they were right-handed as asked, 4) if they used right-to-left writing and if so how often (the theory we use on the spatialization of numbers considers that since childhood we are used to the fact that the progression system goes from left to right (writing, timeline.
They had to concentrate on the screen while Tobii Studio protocol was running and follow the instructions. We recorded the data on a PC computer and saved them on an external disk at the end of each experiment. We used two setups in order to record eye tracking from two participants at a time. We disinfected the material between each participant. The whole session lasted less than 1 hour. To test our protocol before going to the NLP hypothesis, we did a preliminary control experiment testing data already published in the literature. When the subject is doing spatial geometry tasks, and thus working mainly with the right hemisphere, the gaze is usually going to the opposite direction, i. e. to the left. On the contrary, when doing language tasks (such as searching for synonyms), thus working mainly with language areas located in the left hemisphere, the gaze usually goes to the right  (Dehaene, 2010).
Similarly, as we culturally have a spatialized vision of the numbers on a horizontal line, with small numbers on the left and big numbers on the right, imagining a big number is usually brings the gaze to the right, while imagining a small or negative number is supposed to bring the gaze to the left  (Dehaene, 2010). We asked a first series of 40 questions to verify that our protocol was reliable (questions are available in Supplementary Data 3). Questions for the second part of the experiment: We then tested in an independent protocol of 60 questions the hypothesis that different cognitive processes could orient the gaze in different directions. We created and recorded a series of questions (available in Supplementary data 3). As a control, we have used questions that require little thought, therefore supposed to induce little or no eye movement.
Equipment To record the gaze orientation, we used the system proposed by Tobii (Tobii Pro X3-120 eye-tracker). This eye tracker system is composed of an illuminator that emits infrared waves, a camera and a processor that analyses the information and converts the video into data (Figure  2 ). The gaze precision of the eye tracker was 0. 24° and the gaze accuracy was between 0. 4° (in ideal conditions) and 0. 7° (in the peripheral area of the screen). Data were postprocessed with Pro-Lab.
Questions were presented in blocks of different question types in a random order in each block (Supplementary Data 3). We took a short break (10 min) between the two series of questions. Before starting the recordings, each participant was trained with a tutorial protocol to familiarize with the question format. After creating the file to conduct the experiment, we defined the different areas of interest (AOI) for each video. Indeed, Tobii Pro Studio software can compute the data in order to give a series of parameters (such as the percentage of time spent in the AOI, the delay before the first entry in the AOI, the number of visits to the AOI, etc. ) for each defined AOI that we are interested in. We placed the AOI temporally just after the disappearance of the fixating cross, at the end of each question, for a total duration of 5 seconds.
Tobii is constantly recording the fixations and the saccades. During the time of presence of the AOI, we selected the following parameters among the great number of parameters calculated by the Tobii software: Number of fixations of the AOI, Total fixation duration of the AOI, Time to first fixation of the AOI, Total duration of visits of the AOI, Number of visits of the AOI. We exported the data as an Excel format for analysis. Data concerning the fixations preprocessed by Tobii (x and y coordinates of the fixations) were exported in R (version 4. 0. 3; R Core Team, 2020) and we wrote a program aiming to determine the angle and the amplitude of each fixation vector relative to the center, which allowed us to refine the AOI. Some of the participants had a lot of missing data for certain items (looked outside the screen).
We first compared the data obtained for the control questions to those obtained with the other questions to see if there was a difference in gaze movements. We created a binomial variable "is. fix. inside. center" which indicated if each fixation is inside the control area (1) or not (0). Then, we analyzed this variable through a generalized linear regression mixed-model including one fixed effect (i. e. , condition) and 2 random effects (i. e. , subject and item). The R syntax was the following: glmer(dependent variable ~ condition +(1+condition|Subject) +(1|Item), data=df, family=binomial). Because the goal of this analysis was to compare the number of fixations inside the control area for the control condition versus each of the other conditions, we defined the control condition as the intercept.
These results suggest that when some cognitive activity is engaged, people tend to move their eyes more. The next step was to determine if the type of cognitive activity engaged would determine the gaze direction. Therefore, we computed a second analysis to examine the distribution of the gaze orientation according to the question types. In a second analysis, we investigated whether gaze orientations were determined by question types. We observed that within the same question, and within questions of the same condition, there was more than one preferential direction. Figure  8  gives an example of the data obtained with 6 questions of the same type (questions type 1). Each histogram represents the number of fixations in each zone for all participants for each question separately. Percentages are the proportion of fixations in each zone, with zone 5 (center) being excluded.
At the beginning of the tests we did two calibrations: a general one with the Tobii Manager software and a second one just before recording with Tobii Pro. However, the calibration takes into account not only the distance of the subject from the screen but also the position of the subject's head. For the data to be correct, the subject should remain motionless throughout the experiment. In our experiment, to remain in an ecological situation, participants were free to move (no chinstrap or landmarks where to place their heads, etc). Over a cumulative 30 min of testing, we can easily assume that people did not remain static throughout the experiment. This could have affected the accuracy of the measures, linked to the calibration, as the subjects could have slightly modified their position (distance to the screen or slight shift of the head) during the experiment.
Conclusions The results of this study show that there are more eye movements in response to questions that require more mental activity than in response to control questions requiring less mental activity. The study goes even further, by showing that for each type of question, there are one or more preferential directions correlated with the type of question. However, these directions are different from those claimed by the NLP theory. We evaluated our protocol by a control experiment but we did not completely replicate the data from the literature. Therefore, the present results should be taken with utmost caution. This is likely due to the low number of participants and the small number of reliable first fixations recorded. To get around this obstacle, we studied the results obtained in all the fixations recorded following the end of the question during 5 seconds, which is not exactly what we planned to do at the beginning.
Acknowledgements The authors would like to thank all the volunteers that participated this study. The authors greatly acknowledge the  CocoLab (Complexity and Cognition Lab, MSHS Sud-Est  (USR3566),  Université Côte d'Azur -CNRS)  and its staff, particularly  Ambre Denis Noël  for her advices and help in the statistics and  Jean Charles Briquet Laugier  for logistic. We thank  Laurent Michau  (Psycho-practitioner therapist in brief therapies: hypnosis and NLP) for his ad-vices on the protocol. We thank  Olivia Vidal  and  Ilan Sansoni  for their help during the experiments and  Patricia Bouret  for her statistical advice. This work was supported by the  French government  through the  UCA -Jedi project managed by the  National Research Agency  ( ANR-15-IDEX-01 ) and, in particular, by the interdisciplinary  Institute for Modeling in Neuroscience and Cognition (NeuroMod)  of the  University Côte d'Azur.