dataesr/scientific-paragraphs-categorization huggingface dataset:
The tool is geared towards IPC experts and environmental hygiene managers to help them identify areas for improvement in their facilities. It has been in development since 2018, and consists of a 96-question secure, online tool based on a multimodal improvement strategy. A pilot study using an earlier version of the tool was conducted by Clean Hospitals in 51 healthcare facilities in 35 countries  [1]. It was then further developed with the help of an international expert group and validated internationally in seven additional countries. The HEHSAF is published on the online platform RED-Cap, a secure web application for building and managing online surveys and databases. It is specifically geared to support online and offline data capture for research studies and operations. Available exclusively to institutions, REDCap is used by the most prestigious universities around the world.
Currently over 6600 institutions in over 150 countries trust the security of the platform  [2]. Although healthcare facilities who complete the HEHSAF will have access to their own detailed data, only anonymized and aggregated data will be shared. This is of utmost importance to ensure that healthcare facilities are comfortable being transparent when filling out the tool. With global implementation, the HEHSAF will give institutions a roadmap for improving their environmental hygiene, support environmental services staff, increase the visibility of healthcare environmental hygiene on a global level, and save lives by reducing healthcare-associated infections. The HEHSAF and a full Clean Hospitals Day toolkit can be found on the Clean Hospitals website (www. Clean Hospi tals. com). The tool is also available at the following link: https:// redcap.
The publication may also be distributed here under the terms of Article 25fa of the Dutch Copyright Act, indicated by the "Taverne" license. More information can be found on the University of Groningen website: https://www. rug. nl/library/open-access/self-archiving-pure/taverneamendment. Take-down policy If you believe that this document breaches copyright please contact us providing details, and we will remove access to the work immediately and investigate your claim. Downloaded from the University of Groningen/UMCG research database (Pure): http://www. rug. nl/research/portal. For technical reasons the number of authors shown on this cover page is limited to 10 maximum. Download date: 16-07-2023.
Using the cage as the experimental unit, a sample number of six was calculated to be the most appropriate sample size based on data from previous studies (Supplementary Data). Additional animals were included in the HD study to compensate for the high attrition rate experienced with this model. Therefore, 9 to 11 cages of hemizygous mice were included in the study to avoid under-powering the later time points. Data from all cages were included in the analysis and appropriate statistical methods (described below) were used to account for the differences in the group sizes. Mice were housed with colony-mates born within the same week into cages containing animals of the same genotype. Three days prior to recording sessions, the animals were transferred to clean home cages with fresh bedding, nesting material and a cardboard rodent tunnel as enrichment material, in line with the standard husbandry procedures for IVC cages.
Timeframes of interest The timeframes of interest in the current study were defined as the 30 minutes directly preceding lights being turned on (06:30 to 07:00) and 30 minutes directly preceding lights being turned off (18:30 to 19:00). This definition was consistent between both parameters of interest: distance moved (mm) and time spent climbing (seconds). When analyzing activity during the timeframes of interest, we first summed data per time bin (6 min) for mouse within a cage. We then calculated the average activity per cage across the 5 time bins. We have previously shown that C57BL/6J mice show peak activity in the dark phase, and that mouse activity varies around change in light phases in a strain-specific manner  (Bains et al. 2016 ). In the current study these changes were particularly relevant, as mouse models of neurodegenerative diseases, including HD, have known sleep disturbances.
Post-hoc analysis We conducted pairwise post-hoc comparison tests by computing the estimated marginal means (leastsquares means) for factor combinations and correcting for multiple comparisons using the Benjamini-Hochberg method to decrease the false discovery rate. This process was run using R's "emmeans" function, which returned adjusted p values. These values were used to indicate the statistical significance of the genotype effect at various levels of factor combination. While the algorithm for automated behavior annotation is proprietary, the analysis is openly available as a part of this manuscript; please see supplementary material. The datasets for the experiments in this manuscript are also openly available on request. All climbing data were converted from number of frames to time spent climbing in seconds prior to analysis and visualization, as the authors believe that to be a more intuitive parameter.
We have previously shown the capabilities of the system in investigating cage-floor activity in group-housed mice in their home cages  (Bains et al. 2016 ). Here, we show that measuring climbing is part of the standard motor behavior repertoire of mice and can greatly enhance the existing dataset to investigate motor phenotypes in much greater detail and with minimal experimenter intervention. Sexual dimorphism in climbing behavior has been reported previously in singly tested C57BL/6Ntac mice, using the LABORAS system, in which the main aim of the study was to investigate the difference in response to novelty between the sexes. Furthermore, parameters were only measured for 10 minutes  (Borbélyová et al. 2019). More recently, a study on the effect of age, sex and strain on cage-lid climbing in single-housed mice has also reported sexual dimorphism, as well as strain and age differences, in single-housed mice, over 24 hours, peaking in the dark phase  (Zhang et al.
Background Adverse events of special interest (AESIs) were pre-specified to be monitored for the COVID-19 vaccines. Some AESIs are not only associated with the vaccines, but with COVID-19. Our aim was to characterise the incidence rates of AESIs following SARS-CoV-2 infection in patients and compare these to historical rates in the general population. Methods A multi-national cohort study with data from primary care, electronic health records, and insurance claims mapped to a common data model. This study's evidence was collected between Jan 1, 2017 and the conclusion of each database (which ranged from Jul 2020 to May 2022). The 16 pre-specified prevalent AESIs were: acute myocardial infarction, anaphylaxis, appendicitis, Bell's palsy, deep vein thrombosis, disseminated intravascular coagulation, encephalomyelitis, Guillain-Barré syndrome, haemorrhagic stroke, non-haemorrhagic stroke, immune thrombocytopenia, myocarditis/pericarditis, narcolepsy, pulmonary embolism, transverse myelitis, and thrombosis with thrombocytopenia.
Age-sex standardised incidence rate ratios (SIR) were estimated to compare post-COVID-19 to pre-pandemic rates in each of the databases. Findings Substantial heterogeneity by age was seen for AESI rates, with some clearly increasing with age but others following the opposite trend. Similarly, differences were also observed across databases for same health outcome and age-sex strata. All studied AESIs appeared consistently more common in the post-COVID-19 compared to the historical cohorts, with related meta-analytic SIRs ranging from 1. 32 (1. 05 to 1. 66) for narcolepsy to 11. 70 (10. 10 to 13. 70) for pulmonary embolism. Interpretation Our findings suggest all AESIs are more common after COVID-19 than in the general population. Thromboembolic events were particularly common, and over 10-fold more so.
Implications of all the available evidence Our study shows considerable database heterogeneity across the AESIs, suggesting individual study estimates should be interpreted with caution. Comparing the 'Subjects with COVID-19' to the 'Pre-Pandemic Background Population' shows a fairly consistent elevated rate in experiencing an AESI within 90-days after index. The results of this work are of public health importance as they help to put into perspective the risk of the AESIs post vaccination versus post SARS-CoV-2 infection.
Throughout the roll-out of the COVID-19 vaccines, adverse events of special interest (AESIs) have been prespecified to be monitored by medicines regulatory authorities. To provide context for these safety investigations, Li et al. ,  5  in a multinational network cohort study, reported heterogeneity in the background incidence rates of 15 AESIs across age and sex stratification as well as various administrative claims and electronic health records (EHR) databases. Against this backdrop, some vaccine-related adverse events have gone on to be associated with the vaccines; two within the originally pre-specified 15 AESIs (myocarditis and pericarditis  6  and Guillain-Barré syndrome (GBS)  7  ), and one newly-identified AESI (thrombosis with thrombocytopenia syndrome  8  ). It is, however, important to note that some AESIs are not only potentially associated with the COVID-19 vaccines, but with COVID-19 itself.
And in order to understand the benefit risk of COVID-19 vaccines it is important to contextualise this with expected rates following COVID-19. To address this problem, the Observational Health Data Sciences and Informatics (OHDSI) 9 community carried out a network study using observational data from 26 databases across 11 countries with the aim of estimating the incidence rates of 16 vaccine related AESIs among people who had COVID-19, as compared to the background population during the pre-pandemic phase.
Data sources Rates were obtained from 26 databases, which included 8 administrative claims databases, 12 EHRs, 1 EHR with a registry, and 5 general practitioner (GP) databases. These databases represented 11 countries: Belgium, Estonia, France, Germany, Japan, the Netherlands, Serbia, Spain, Turkey, the United Kingdom (UK), and the United States of America (US). All of these databases represent subsets of the total population from which they originate. We defined the 'Pre-Pandemic Background Population' cohort as patients who were observed in a database on the index dates 1 January 2017, 1 January 2018, or 1 January 2019 (i. e. , the patient was active in the database on one or any of those dates they would be included in this cohort). The entry event was the date observed and patients were only included if they had at least 365 days of observable time prior to the index date.
Research Protection Program (HRPP The 'Patients with COVID-19' cohort was defined as persons with a positive laboratory test for SARS-CoV-2 (any SARS-CoV-2 laboratory testing method) or an observed diagnosis code for COVID-19 (without a negative test on or within 3 days after index). The index date, or entry event, was set to the first of these and did not have to occur on a specific month or day. Additional inclusion rules included keeping patients that had at least 365 days of observable time prior to the index date and having no diagnosis of COVID-19 or SARS-CoV-2 prior to index. The calendar time for this cohort included all time available within each database after 1 December 2019, see Table  1  for specifics (e. g. , CPRD_AURUM last date of capture is March 2021). An additional 'Patients with COVID-19' variation cohort was created for sensitivity analysis; it was a similar cohort except with the additional inclusion rule that cohort entry had to be prior to the COVID-19 vaccines becoming available (i.
e. , index before 1 January 2021); this cohort was called 'Patients with COVID-19 prior to 2021'. Patients were not excluded from the COVID-19 cohorts if they were also in the 'Pre-Pandemic Background Population'. ] [19] [20]  All databases contributed to both study cohorts ('Pre-Pandemic Background Population' and 'Patients with COVID-19'), except for FIIBAP and IU, which only contributed to the 'Patients with COVID-19' cohort as these databases only contained COVID-19 patients and not a general population. This study uses coded data that already exist in an electronic database. Confidentiality of patient records was always maintained. All study reports contain only aggregate data and do not identify individual patients or physicians. At no point during the study was identifying information about the subjects used.
Statistical analysis We defined time at risk as 1-90 days after the study participant index date. Patients contributed time-at-risk from the index date until the earliest of 90 days after the index, they leave the database, or the start date of the AESI event. Death was not explicitly used as a censoring criteria, as the capture of death varies within each database, including some databases that do not capture death at all. We do censor patients at the end of observation, whether that be death or other reason for loss-tofollow-up. We also excluded individuals for a specific outcome if a prior event had occurred during the clean window for that outcome. A clean window is the minimum time between event occurrences to be considered a new event. We used an outcome-specific clean window, within which an outcome is not considered incident.
The clean window was 365 days for all outcomes except 30 days for anaphylaxis and 183 days for facial nerve palsy and encephalomyelitis. 21 The clean window was set different for anaphylaxis, facial nerve palsy, and encephalomyelitis to better define incident cases of these AESI. Incidence rates were calculated as the total number of events divided by the person time at risk. The incidence rates were stratified by age and sex subgroups for each database. The age subgroups were (in years): 0-5, 6-17, 18-34, 35-54, 55-64, 65-74, 75-84, and ≥85. The age-specific rates for the AESIs were pooled across the databases using a random effects meta-analysis, with the DerSimonian-Laird method to estimate between database variation. The 95% prediction intervals were calculated using the R package "meta".
Prediction intervals reflect the expected uncertainty if an estimated rate from another study were included in the metaanalysis. Indirect standardisation  22  was used to account for differences between the age subgroups and sex distribution in the COVID-19 disease cohort and the 'Pre-Pandemic Background Population'. Within each database, the pre-pandemic cohort was used as the demographic reference, such that an expected incidence rate could be computed using COVID-19-specific incidence rates weighted to reflect the 'Pre-Pandemic Background Population' demographics. COVID-19 cohort observed and expected rates were compared using standardised incidence ratios (SIR) with corresponding exact 95% confidence intervals. Negative control outcomes were also used to evaluate potential bias in incidence ratio estimates.
Twenty negative control outcomes were selected as diseases with no a priori evidence of a causal relationship with COVID-19, based on literature review and clinician adjudication (Appendix 3). Incidence rate and SIRs for these negative control outcomes were estimated in the same manner as for the 16 AESIs. Meta-analytic age-and sex-specific rates were classified using the WHO Council for International Organizations of Medical Sciences thresholds: very common (≥10%), common (>1%-<10%), uncommon (≥0. 1%-<1%), rare (≥0. 01%-<0. 1%), and very rare (<0. 01%). 23 ll analyses were performed in R. The study protocol and analysis code are available at https://github. com/ ohdsi-studies/Covid19SubjectsAesiIncidenceRate.
Results We included 23,840,986 'Patients with COVID-19' from 26 databases representing a diverse set of care settings from North America, Europe, and Asia (Table  1 ). The percentage of females found across the databases ranged from 47. 4% to 56. 1%; all databases had slightly more females than males, except IMASIS and JMDC. The average age of persons in each database ranged from 24 to 72 years, reflecting the differing patient populations covered across the databases (e. g. , IBM_MDCD has a large child and childbearing-aged female population, while IBM_MDCR represents retired individuals). The 'Pre-Pandemic Background Population' represented more than 492,730,503 person records. Characterisation results produced by CohortDiagnostics. 24 can be found on an interactive web app (https://data.
ohdsi. org/Covid19SubjectsAesiIncidence Rate/). Fig. 1  plots the incidence rates for the 'Patients with COVID-19' population, stratified by age group (x-axis), sex (half the x-axis represents females and the other half males), and database (color-coded by database type: claims, EMR, and GP centric data), trellised by the 16 AESIs. Similar age trends across databases were observed. Some AESIs had a clear increase in incidence rate with age: AMI, non-haemorrhagic stroke, DVT, PE, haemorrhagic stroke, Bell's Palsy, TWT, ITP, DIC, and GBS. In contrast, some AESIs had a clear decrease in incidence rate with age: appendicitis and anaphylaxis. Finally, some age trends were less clear: myocarditis and pericarditis, narcolepsy, encephalomyelitis, and transverse myelitis. Fig. 1  also shows substantial database heterogeneity within individual AESIs.
For example, for DIC in the 90 days after COVID-19 diagnosis, males aged 35-54 in JMDC experienced the event 2417 per 100,000 person-years as compared to CPRD which experienced the event 5 per 100,000 person-years. Additionally, the incidence rates for 'Patients with COVID-19' were compared to the 'Pre-Pandemic Background Population' in Appendix 4. For most outcomes, across all database/age group/sex combinations, the incidence rates are higher for the 'Patients with COVID-19' compared to the 'Pre-Pandemic Background Population'. To help give an overview of these results, Fig. 2  shows the pooled estimated age-and sex-stratified incidence rates per 100,000 person years (PY) for AESI events in the 90-days after index for 'Patients with COVID-19'. Nevertheless, the incidence of several AESIs differed greatly between age and sex strata.
Pulmonary embolism had the highest SIRs (11. 7 [95% confidence interval 10. 1-13. 7]), suggesting that the observed incidence of pulmonary embolism in the 90 days after COVID-19 diagnosis was over 11 times higher than expected in a 90-day period for the background population in the pre-pandemic period. The database specific results are reported in Appendix 6. The negative control outcome estimates are provided in Appendix 7 and reveal consistent positive bias in estimated SIR values. SIRs ranged from 0. 9 to 5. 0 with a majority of generated SIRs above 1. 7. The meta-analysis was repeated for 'Patients with COVID-19 prior to 2021' to see if inclusion of COVID-19 vaccinated patients may have impacted the results however the results found were similar (Appendix 8).
Discussion To our knowledge this is the largest study to date on the descriptive epidemiology of AESIs among the COVID-19 population. In this study we estimated age-and gender-specific incidence rates of 16 AESIs among COVID-19 patients using 26 observational data health sources from around the world. To contextualise our findings, we reported on the age-and gender-SIR, comparing the IR among COVID-19 cohort to the 'Pre-Pandemic Background Population' of the databases. Our findings suggest a consistent trend of an increased risk for multiple AESIs among the COVID-19 cohort when compared to the database 'Pre-Pandemic Background Population', though the magnitude of the risk should be interpreted with caution. We found considerable heterogeneity in the IR among the COVID-19 cohort reflected by the wide prediction intervals of the pooled age-and sex-specific IR, suggesting that caution is needed when these estimates are used.
We also observed considerable variability with age and some with sex groupings, emphasising the need for age-and sex-stratification when assessing risks and benefits of COVID-19 vaccines. The observed magnitude of heterogeneity across sources within age and sex subgroups suggests that residual differences are present. The remaining heterogeneity may be related to differences in healthcare systems, settings, data capture processes, or true differences in subpopulations. A limitation of this work is we have not furthered stratified by comorbidities related to either COVID- 19      or obesity) or socio-economic status. These differences may also be due to systematic error, selection bias, or differential outcome measurement error between databases. 25 In a similar analysis across 13 databases, Li et al.
5  reported that AESI background rates varied by database and regions. Consistent to our findings, the authors also found considerable differences in the incidence rates by age and sex, suggesting caution is needed when incidence rates are compared across populations. 5 mong COVID-19 patients, the majority of COVID-19 AESIs were found to be "uncommon" with few "rare" among all age and gender groups investigated. Thrombotic events such as AMI, strokes, DVT, and pulmonary embolism were more frequent compared to other AESIs and were "common" in older COVID-19 patients. These findings are consistent with prior studies suggesting that cardiovascular and thrombotic complications in particular are relatively common post COVID-19,  26, 27  especially among older patients. In most databases, the risk of these thrombotic events was higher among COVID-19 patients when compared to the 'Pre-Pandemic Background population' of the databases with a pooled SIR above 3.
In particular, the SIR for pulmonary embolism had an elevated trend away from the null in all databases but one. It is well established that COVID-19 is associated with a hypercoagulable state, and older patients with additional risk factors have a worse outcome. Consistent with our results, clinical evidence suggests that COVID-19 is particularly related to pulmonary embolism  28  especially among those with pneumonia. However, until now, the precise incidence of thrombosis in patients with COVID-19 has not been determined, mainly because multiple studies have reported conflicting estimates. 29 he Centers for Disease Control and Prevention (CDC) lists myocarditis and pericarditis, thrombosis with thrombocytopenia syndrome (TTS), presented in this work as TWT, and GBS as three serious types of adverse events following COVID-19 vaccination, with evidence that suggests, although rare, a link to certain types of COVID-19 vaccinations.
30 These three adverse events are also listed on the label of at least one COVID-19 vaccine authorised for use in the US and Europe. Our findings suggest that, while still rare among COVID-19 patients, the risk of these three events may be higher in the COVID-19 patients when compared to the 'Pre-Pandemic Background Population'. Since the estimated incidence ratios are unadjusted and are not presented for causal inference, future investigation may be warranted to fully contextualise the risk benefit balance of the COVID-19 vaccines as it relates to myocarditis and pericarditis, TWT, and GBS. ] [33]  Previous studies have also found that myocarditis and pericarditis may be part of the wide spectrum of cardiovascular sequelae of COVID-19 disease. 32, 34, 35 A CDC network retrospective cohort study  34  using data electronic medical records across multiple databases in the US, reported that among 814,524 COVID-19 patients the incidence of myocarditis or pericarditis varied by age and gender and ranged between 17.