dataesr/scientific-paragraphs-categorization huggingface dataset:
Discussion: The core protocol provides a shared uniformity across trials, enables a pool of shared controls, and reduces the total number of patients necessary per trial, limiting the number of patients on placebo. All VWM clinical trials are suggested to adhere to the core protocol. Other trial components such as choice of primary outcome, pharmacokinetics, pharmacodynamics, and biomarkers are exible and unconstrained by the core protocol. Each sponsor is responsible for their trial execution, while the control data are handled by a shared research organization. This core protocol bene ts the e ciency of parallel and consecutive trials in VWM, and we hope accelerates time to availability of treatments for VWM.
Vanishing White Matter Vanishing White Matter (VWM, OMIM #603896) is a rare leukodystrophy caused by biallelic pathogenic variants in any of the ve genes EIF2B1-5, encoding the ve subunits of eukaryotic initiation factor 2B (eIF2B). The only known epidemiological data come from the Netherlands, where the incidence is approximately 1:100,000 live births and the prevalence is approximately 1. 3:1,000,000 inhabitants  (1). The incidence and prevalence in other countries is likely similar based on studies of genomic databases  (2, 3) , making it an orphan disease. VWM is characterized by chronic decline with stress-induced episodes of rapid decline, followed by death or partial or complete recovery. Patients develop motor and cognitive disabilities and die after years of progressive handicap  (1).
Rationale The core protocol is designed as a template for phase 2/3 clinical trials in VWM with the purpose to collect safety, tolerability, and e cacy data for marketing authorization (including conditional approval, exceptional circumstances, and accelerated approval) and economic evaluation and bene t-risk assessment as part of health technology assessment (HTA). We combine phase 2 and phase 3 to use the low number of eligible trial candidates e ciently. The template establishes core features for separate trials including a description of the study population, age-speci c endpoints, inclusion and exclusion criteria, study schedule of assessments, randomization plan, sample size determinations, and a statistical analysis plan. The core protocol comprises a xed part, to which participating sponsors are obligated to comply.
In addition, there is a exible, unconstrained part, in which other details, such as pharmacokinetics, pharmacodynamics and biomarkers, can be added (Fig. 1 ). The sponsor is free to choose the primary outcome measure. Each trial arm is executed by a sponsor with its own Contract Research Organization (CRO). So, the design harmonizes clinical trial execution in VWM, but does not operate as a single multi-arm multi-stage trial, such as a platform trial. The use of this core protocol enhances e ciency in assessing new therapeutic agents in VWM, because uniformity across trials allows the pooling of data from the control arms and comparison of treatments.
The relapsing-remitting disease course combined with a chronic deterioration of VWM imposes additional constraints on data analysis. Episodic deteriorations introduce noise in the data, for which analysis should be corrected. One of the solutions is to separate the mean trend from intra-personal variance by increasing the frequency of measurements. It is, however, necessary to cap the total number of measurements to limit the burden on patients. In this core protocol, patients are assessed at least 6 times: at baseline and 3, 6, 12, 18 and 24 months.
Sample size and level of power Considering the very low number of available VWM patients, overall as well as per age group, a classically powered RCT is not considered feasible in VWM. The conventional statistical analysis of a double-blind RCT is based on a power of 80% with two-sided testing, testing, and an alpha of 0. 05. Strategies for substantially reducing sample sizes include setting the power to 60%, replacing two-sided testing with one-sided testing, and increasing alpha to 0. 10. Such settings are common in phase 2b trials and are considered acceptable for ultra-rare diseases  (33). For this proposal, historical data on ambulation and HUI scores, collected in the VWM natural history cohort  (1) , are used for sample size calculations. Single attribute scores, describing one domain of function, range from 1 (best) to 0 (worst).
For patients < 6 years of age, a single-arm open-label study using historical controls for comparison has been recommended previously  (3). The best data available for this age group is the HUI generic score (3). We chose a HUI generic score of > 0 to exclude patients with a very low level of functioning. Based on data from the VWM natural history study (1), the average annual decline in HUI generic score in patients with rst assessment < 6 years and a baseline HUI score > 0 is estimated to be -0. 054 points with a residual within-subject variance of 0. 018, a between-subject intercept variance of 0. 086, a betweensubject slope variance of 0. 002, and between-subject intercept-slope covariance of -0. 001. The parametric bootstrap estimate of the standard deviation of the individual slopes was 0.
In order to facilitate sharing of control data from different trials, adequate serum and plasma (30 ml blood for patients ≥ 6 years) should be collected at all trial visits. The shared controls samples should in part be stored at a central CRO or a laboratory employed by this CRO, so that subsequent trials have the opportunity to compare their biomarker results. Importantly, the primary, secondary, and exploratory outcome measure(s) are decided by the sponsor. It is optional to de ne composite outcome measures. It is up to the sponsor whether additional outcome measures are collected, including patient-de ned outcomes. The sponsor de nes which change in primary outcome measure will be considered signi cant. Thus, for the sponsors of each trial executed using this core protocol, there is signi cant remaining exibility.
Shared control pool To safeguard the integrity of the individual trials, control data will be shared after completion of the trial, for the bene t of subsequent trials. The sponsor owning the data will ensure that data of the placebotreated group is accessible by investigators, other sponsors and regulatory bodies, and guarantee longterm availability of the data. The integrity of the shared control pool will be monitored by the CRO. For each trial, administration mode, dose frequency, and drug-speci c inclusion/exclusion criteria will be addressed in the drug/placebo regimen overview. The risk of bias is reduced by the large effect size aimed for (halting disease progression) measured with robust outcome measures.
Study schedule The minimum trial duration is 2 years. We propose an open-label extension and data collection for all patients until the last included patient has been followed 2 years. The schedule of assessments can be viewed in Table  1  and Fig. 2. A period between inclusion and randomization, known as a run-in period, is not used because (a) it potentially decreases study validity (46), (b) washout periods are not applicable, as the IMP is given next to best supportive care and no other treatments are available, (c) with the variable disease course of VWM a run-in of 3-6 months is too short a time-frame to be su ciently informative, and (d) patients, especially very young ones, may deteriorate irreversibly in this time frame. An observational period after inclusion is not required because disease stability for the preceding 3 months is an inclusion criterion.
Organizational aspects, data management, and implementation The CRO is responsible for randomizing patients over the experimental and control arms of the trial and managing the data. Each trial protocol using this core protocol will be separately registered and submitted to regulatory authorities. After completion of the trial, data of the placebo-treated patients will be shared with a separate CRO arranged in a data transfer agreement between the CRO of the sponsor and the CRO of the shared controls. Once a trial has nished, upon database lock, the CRO will provide the sponsor of the trial with all data of the placebo controls collected in previously completed trials, meaning that the rst trial does not bene t from control data collected in other trials. The shared data include outcomes, descriptives and confounders.
The trial(s) that are still running will continue to be blinded, as are the clinical trial sites. Through the use of the core protocol, the compatibility and interoperability of the core data elements across the different databases is ensured at the start of each trial. A data dictionary with the database structure and detailed descriptions and coding of the data elements is provided by the core protocol CRO and will enable data exchange, even in the presence of different electronic data capture systems. To encourage the use of the core protocol, the VWM Consortium and patient advocates offer several services to sponsors. The validated VWM mouse model has been made available in an independent CRO, to be used for preclinical testing of the IMP. The VWM Consortium can be consulted to evaluate preclinical data and trial design.
Post-authorization evaluation When new products for VWM become commercially available, it is likely that long-term safety and e cacy will still need monitoring. Real-world data collected in a registry can help answering those academic research as well as regulatory and HTA questions  (47) (48) (49). In the case of remaining open questions, such as after conditional, exceptional or accelerated approval (50), the marketing authorization holder often has the obligation to collect post-marketing data to help answer those open questions. Typically, the marketing authorization holder launches a drug-speci c registry or long-termfollow-up study to monitor the authorized drug. We prefer a single academia-led VWM-speci c patient registry that can be used for multiple purposes, including academic research and regulatory/HTA decision-making.
Prior platform trials  (15, 16, 22, 23, 52)  have been able to conduct adaptive multi-arm and multi-disease-stage clinical trials  (20, 21, 27). However platform trials designs are highly complicated (28) and, to our knowledge, are challenging to tailor for heterogeneous ultra-rare diseases with a high unmet need. The present protocol was designed to create a straightforward uniform template for parallel and consecutive trials in VWM. The choice to use a non-randomized trial in patients < 6 years of age with a historical control was di cult. Placebo-controlled trials are standard to demonstrate the causal effects of interventions. However, we believe the rarity, severity, and relative predictability of disease course of VWM at ages < 6 years justify an open-label trial. The VWM registry contains data from approximately 400 patients making it the largest VWM cohort worldwide and can be used to provide historical controls, especially for patients with early onset, which constitutes the largest group.
Besides these four mandatory steps, normalization, grouping, weighting, and additional LCIA data quality analysis are optional steps within the LCIA phase. Social and economic assessments should be performed within the same framework and for that purposes social LCA and economic LCA (Life Cycle Costing) were developed. Currently, LCA standards related to the built environment are EN 15804 (product),  7  EN 15978 (buildings)  8  and EN 17472 (civil engineering works),  9  whereas standards related to the Built Social and Economic assessment are, respectively, EN 16309-A1  10  and EN 16627  11  concerning buildings. EN 17472  9  for civil engineering works includes social, economic, and environmental aspects. Compared with environmental LCA, economic and especially social LCA were rarely applied in the sustainability assessment of recycled aggregate concrete (RAC) structures in the previous research-these methodologies are less systematized currently.
| System boundaries and LCI data modeling A basic methodological issue in applying LCA for RAC structures is modeling the concrete waste recycling. The choice of system boundaries and LCI data modeling depend on the way concrete recycling is modeled, which is directly related to the type of LCA that is performed. [12] [13] [14] [15] [16] [17]  Generally, two types of LCA are distinguished: attributional (ALCA) and consequential (CLCA). In assessments using an attributional approach, the purpose is to estimate the impacts of a product system within cradle-to-grave boundaries, at a given point of time assuming a status-quo situation. A consequential approach on the other hand is intended to estimate the environmental burdens that occur, directly or indirectly, as a consequence of a certain decision.
Such a procedure is applied for instance in the Ecoinvent database where uncertainty factors based on expert judgments are used. 69  The uncertainty estimations on the level of a unit process are then obtained using Monte Carlo simulation. However, it is stated in this Ecoinvent report  69  that "the deterministic results are regarded as more reliable than the probabilistic mean values because they are often based on roughly estimated distribution parameters," which have to be used for probability distribution definition and Monte Carlo simulations. Besides, there can be situations when the probabilistic approach does not help to rank, although it provides a "measure" of uncertainty (or degree of reliability). If the mean values are similar, a clear ranking between the alternatives can be identified if we consider them only.
For instance, Mostert et al. 72  chose to perform the uncertainty analysis regarding the variability of the concrete constituents' content, energy consumption in the EoL phase, and transport distances. They found in their specific case study that CoV of the aggregated LCA results for raw material consumption, water use, cumulative energy demand, and GWP ranged between 1. 8% and 8. 8%, respectively, with higher uncertainty belonging to RAC compared to NAC structure. Hafez et al. 73  established several important sources of uncertainty in concrete LCA: upstream data regarding raw materials depending on the source used, the selected characterization factors in the midpoint approach, service life prediction, the energy use in forecasted technologies for maintenance, demolition, and EoL, CO 2 uptake model.
It is usual to take directly EPDs of aggregates (especially natural) using reference databases or manufacturers' EPDs for LCA studies on NAC and RAC. Table  3  shows impact category indicators per kg of aggregate taken from Ecoinvent V2. 0 database,  76  French Manufacturer database,  19  Josa et al. data used in a concrete LCA study,  77  and Won-Jun et al. data assessed in a specific Korean study. 78  In the case of the French EPD of RCA,  19  impacts due to the reduction of block size is excluded and allocated to the parent concrete, which leads to decreased impacts. Won-Jun et al. 78  assessed the influence of process production (dry/wet) of RCA on environmental impacts in comparison to production of NA using the ISO 14044 standard in the Korean context. The environmental impact of the wet process (RCA) was found to be up to 16% $ 40% higher compared with the dry process due to the energy used by impact crushers while producing wet RCA.
Finally, uncertainty analysis should include uncertainties due to different concrete mix design methodologies since they can have a larger impact on the LCA results than for instance, LCI data or scenario uncertainties. Despite the discrepancies found in LCA studies dealing with the environmental assessment of concrete and concrete structures, the following conclusions are generally valid: • the contribution of various phases to total life cycle impacts depends on the type of construction. For buildings, life cycle impacts are often dominated by energy consumption during the use phase: it is estimated that the use phase in conventional buildings represents $80% to 94% (for a common house, not a passive house) of the life cycle energy use, while 6% to 20% is consumed in materials extraction, transportation and production, and less than 1% is consumed in EoL phase.
Impact category indicators per kg of concrete constituent materials were taken from European manufacturers' EPDs for cement, plasticizer, and reinforcement  [84] [85] [86] [87]  or calculated using the Ecoinvent V2. 0 database for aggregate and concrete production. 76  RCA data was taken from Josa et al. 77  It is evident that the contribution of other phases in the concrete production process is for an order of magnitude (and even more) smaller compared to the cement production contribution. Only comparable to it is the reinforcement production but with exception of GWP which is smaller in the reinforcement case. Transportation of constituent materials to the concrete plant was not taken into account since it largely depends on assumed distances (according to the territorial network) and types.
| Assessment of social impact categories and indicators A complexity inherent to the nature of social impact indicators is the data collection methodologies. Social indicators tend to be more subjective, and fewer data are usually available at large scales. The assessment of social indicators may be done quantitatively or qualitatively. In the case of using quantitative indicators, data can be obtained from open databases from governments or results of surveys distributed to representative samples. In the case of qualitative indicators, these may be collected through interviews, small-scale surveys, or expert seminars. Usually, they are represented in the form of scales, such as the Likert scale. For instance, Sou et al. 94  evaluated the indicators using questionnaires distributed among engineering consultants, government officials, and academic institutions.
The subject's head is not fixed with respect to the sensors and any movement can reduce data quality markedly. The SQUID-MEG systems are bulky, making the experimental environment unnatural and severely limiting the experimental paradigms that can be utilized. These limitations impede the use of MEG for many subject groups, including children and patients with various pathologies. A new generation of MEG sensors is emerging: the optically pumped magnetometers or OPM. In OPM, an atomic gas enclosed in a glass cell is traversed by a laser beam whose modulation depends on the local magnetic field. Alkali-based OPMs were developed first  [3]. They have very good sensitivity (15 fT/ √ Hz in dual-axis mode) and can be placed near the scalp, potentially allowing a three-to-eight-fold increase in signal power of neuromagnetic activity recording  [4] [5] [6] [7].
Data Analysis All data were analyzed using MNE-python (version 1. 1. 1 [31] ) in a Linux environment and were as similar as possible for SQUID-MEG and 4 He-OPM data while optimizing data quality for each modality. For both datasets, breaks during acquisition longer than 9 s, as well as spiking artifacts (fast amplitude deviations) larger than 3 standard deviations (sample-to-sample), were marked to be ignored. Due to the presence of low frequency drifts in the 4 He-OPM data, (slow) amplitude deviations larger than 5 standard deviations (relative to signal average) were also removed. For SQUID-MEG data, third-order gradient compensation was applied  [32]. For ICA decomposition only, the data were downsampled to 500 Hz and bandpass filtered between 1-100 Hz. ICA components related to eye movements, blinks, heartbeat and gross non-neural artifacts were removed.
1 s, -0. 02 s] for somatosensory, [-0. 2 s, 0 s] for visual) standard error over epochs. Time windows were chosen to be as long as possible while avoiding artifactual contamination. Note that there was no delayed response in the visual task, and responses started to occur > 300 ms. SNR values were computed for each sensor and subject individually. To ensure a comparable estimation, the SNR estimate was corrected for the number of trials in the individual dataset by taking a subset of trials from the modality containing a larger number of trials. This was repeated (1000 iterations) to eliminate selection bias. Empty room recordings: To quantify the raw performances of SQUID-MEG and 4 He-OPMs sensors, we performed empty room recordings with identical acquisition parameters as described above except for the recording duration, which was set to 30 s.
SNR. As can be seen in Figure  8 , SNR values again show a significant overlap between SQUID-MEG and 4 He-OPMs. For the visual task, 4 He-OPM SNR was a bit lower than in the somatosensory task (SNR 14. 1 SD 4. 9 for the radial axis), although SNR is nearly equal for both axes. SQUID-MEG benefits from the highest SNR (26. 0 SD 8. 4). This difference is significant (paired samples t-test: t = 5. 8, p < 0. 001). Note that also here, the range of SQUID-MEG SNR is larger than for the OPM sensors. Oscillatory power. Figure  9  shows the time-frequency re and 4 He-OPMs data for the visual task. Significant cluste marked in black. Both SQUID-MEG and 4 He-OPMs show a p alpha/beta (8-30 Hz) range and a simultaneously elevated g expected with the presentation of visual Gabor stimuli. Alt was lower in the 4 He-OPM sensors than in the SQUID senso at the core gamma response frequency, suggesting a robus radial component.
These results are encouraging when taking into account the fact that we used a template subject to co-register the SQUID and OPM sensors leading to limited spatial accuracy. Time-frequency decomposition of the visual stimulation data reveals significant alpha/beta and gamma oscillatory components that are highly similar across sensor types, showing that the 4 He-OPM sensors can pick up changes in oscillatory brain dynamics across the human oscillatory neural range. However, while the absolute signal is higher in amplitude, the percent signal change from baseline is lower for 4 He-OPM sensors compared to SQUID-MEG, especially in the higher oscillatory (gamma) range. Previous results have shown that gamma band activity can be reliably recorded using OPMs with relative power increases equal to or better than SQUID-MEG  [10].
4 fT/ √ Hz for SQUID-MEG. Of course, the decreased distance to the scalp is a mitigating factor, resulting in data quality nearing the fully developed SQUID-MEG. Besides intrinsic noise, there are important differences to consider when comparing performance between the 4 He-OPM sensors and the CTF SQUID-MEG system used in this study: The OPM sensors were head-mounted, and any head movement produced by the participants caused movements of the sensors in a reduced, but non-homogeneous magnetic field. The (single) reference sensor, mounted 10 cm above the head, captures a significant portion of this movement-related signal, but as it may experience a slightly different field and its movements are slightly different from the scalp sensors, it is not as effective as the CTF integrated reference sensors in reducing environmental noise.
Furthermore, the aforementioned cabling of the sensors, which will be optimized in the next iteration of the system, is likely to induce not only additional muscle contractions but also the slight movement of the sensors relative to the head. Although head movements were not recorded in the current study, we observed significant variation in data quality between participants, likely attributed to movement-related artifacts and/or sensor placement. Within the experimental group, some participants show 4 He-OPM data quality equal to SQUID-MEG, suggesting that the 4 He-OPM system is capable of higher performance than SNR values may currently reflect. Obviously, the ability to use head-mounted sensors with free head movement is a key benefit of using OPMs, but it may also explain the lower performance of the OPM system.