dataesr/scientific-paragraphs-categorization huggingface dataset:
Study population The study period was 28th March 2011 until 4th January 2017. The start date was the earliest point at which the NHFD captured unique patient identifiers that could facilitate linkage to other datasets and the end date was chosen to facilitate 12 months follow-up. The inclusion criteria were those recommended by NICE  [3] : All adults aged > 60. Displaced fracture of the femoral neck that was deemed unsuitable for internal fixation. Independently mobile or using a single stick before injury. Medically fit to undergo hip arthroplasty, defined as an American Society of Anaesthesiologists [ASA] grade < 2  [5]. Patients without substantial cognitive impairment, defined as an Abbreviated Mental Test Score (AMTS) > 8  [5]. We excluded patients that presented to hospitals in Wales, Northern Ireland, and the Isle of Man as HES APC only captures data from hospitals in England.
The final model utilised 1:1 nearest neighbour matching with a 0. 02 calliper (as recommended by Austin  [29] ), no replacement, and the common support restriction. All subsequent descriptive, regression, and survival analyses were confined to the propensity score matched groups. Descriptive statistics Categorical variables were compared using Chi-square tests and non-normally distributed continuous variables using the Kruskall-Wallis one-way analysis of variance test. Length of stay data were only analysed for the proportion of patients that were discharged alive from hospital to prevent left skew caused by early deaths. Survival analysis Kaplan-Meier estimates were plotted with 95% confidence intervals for cumulative survival free from unplanned secondary procedures. The proportional hazards assumption was tested by visual examination and statistical assessment of the relationship between event time and Schoenfeld residuals.
Year of fracture was included as an ordinal variable within regression models to account for the possibility of changing outcomes over time. Multivariable regression Multivariable logistic regression was used to adjust for residual imbalance between the two groups in respect of discharge to own home and 30-day re-admission. The co-variables were as specified above. Length of stay data conformed to a gamma distribution and so were adjusted using generalized linear models (GLM) together with post-estimation calculations of average marginal effects to yield predicted mean differences and 95% confidence intervals. Logistic regression and GLMs utilised cluster-robust standard errors and robust variance estimators  [31]  to account for the lack of independence between matched records  [32]. Propensity score matching was achieved using the MatchIt application for R (R Foundation for Statistical Computing, Vienna, Austria).
Meta-analysis of randomised trials There were 11 previous systematic reviews but none reported analyses limited to patients that were cognitively intact and independently mobile before injury (Additional file 2: Figure  S1 ). The 11 earlier reviews included 16 trial reports, which presented data from 14 individual RCTs. Eight RCTs did not satisfy the restricted inclusion criteria of this systematic review, e. g. they did not exclude patients with cognitive impairment or limited mobility. One study could not be retrieved despite extensive attempts. The reasons for excluding each RCT are shown in Additional file 2: Table  S1. Five randomised controlled trials satisfied the eligibility criteria for this review (Additional file 2: Table  S2 ). Two were based in the UK  [33, 34]  and one each in Sweden  [35] , Italy  [36] , and the USA  [9].
Observational "real world" data There were 143,871 patients with displaced intracapsular hip fractures that underwent HA or THA and could be matched to a record within HES APC (Fig. 1 ). 28,099 (19. 5%) satisfied the pre-specified inclusion criteria, i. e. ASA < 2, AMTS > 8, and independently mobile. The groups initially varied considerably in terms of baseline characteristics (Additional file 3). After propensity score matching, 12,290 cases were selected for further analysis. Table  1  shows that the baseline characteristics of the matched groups were similar. The distribution of propensity scores was also improved after matching (Additional file 3).
Discussion No previous meta-analysis has reported data limited to the fittest patients with hip fractures, which are the patients that national guidelines recommend should be considered for THA  [2, 3]. This study identified five RCTs that compared HA and THA amongst independently mobile older adults with displaced intracapsular hip fractures  [9, [33] [34] [35] [36]. These trials were typically small (median 89 patients) single-centre studies that were limited by few events (pooled totals 11/467 [2. 4%] dislocations, 23/467 [4. 9%] revisions, and 57/467 [12. 2%] deaths). No individual trial reported differences in outcomes and it is even possible that the pooled analyses were underpowered to detect important differences between the groups. We therefore analysed data from the largest available cohort of hip fracture patients and used propensity score matching to replicate randomisation as far as is possible using observational data.
There is however evidence to suggest that the functional benefits of THA become more pronounced over a number of years follow-up  [7]. There is one on-going RCT  [37]  that mighteither in isolation or when combined with data from previous trialsreport sufficient events to identify differences between the two operations. However, the AAOS has expressed concern that the benefits of THA might not be generalisable beyond the controlled environment of clinical trials  [2]. The RCTs identified in this study were all based in large academic centres and two  [35, 36]  specified that operations were only performed by experienced arthroplasty surgeons. Observational datasets can provide important context for RCT findings as they reflect "real world" practice in which operations may also be performed in smaller orthopaedic units, by generalist orthopaedic surgeons, and by trainees.
It is therefore reassuring that, although the propensity score matched cohort mirrored the RCT participants in terms of HA dislocation rate (both 0. 9%), the THA dislocation rate was lower in the observational cohort than reported by trials (1. 6% versus 3. 9%). There were also fewer revisions identified in the propensity score matched cohort than were reported by the RCTs (THA 1. 1% versus 1. 7%; RCT 4. 8% versus 4. 1%). Although it is possible that some dislocations and revision procedures were not captured by the linked dataset, our findings are similar to those of a recent population-based study from Canada  [11]. These authors reported findings that were the same in both magnitude and direction (THA dislocation 1. 9% versus 0. 8%; revision 0. 4% versus 2. 3%) as observed in our study.
Acknowledgements HQIP,  NHS Digital , and the  Office for National Statistics  for supplying data. NHS Digital and Crown Informatics Ltd. for support with data linkage. Chris Boulton  ( Royal College of Physicians ),  Denise Pine  ( NHS Digital ), and  Tim Bunning  ( Crown Informatics Ltd ) for managing data flows. The  Bodleian Library  ( University of Oxford ), The  British Library , and the  Georgetown University Library (USA). Dr. May Ee Png  ( University of Oxford ) for assistance with evaluating studies published in Chinese.
Funding Data access was funded by a  Royal College of Surgeons of Edinburgh (RCSEd)   Pump Priming Grant  and an  Oxford-UCB Prize Fellowship in Biomedical Research. Cheryl K. Zogg  is supported by  NIH   Medical Scientist Training Program Training  Grant  T32GM007205. Andrew Judge  is supported by the  NIHR Biomedical Research Centre at the University Hospitals Bristol NHS Foundation Trust  and the  University of Bristol. Daniel Perry is supported by a  National Institute for Health Research (NIHR)   Clinician Scientist Fellowship  ( NIHR/CS/2014/14/012 ). All authors carried out this research independently of the funding bodies. The views expressed in this publication are those of the authors and do not necessarily reflect those of the  NHS , the  National Institute for Health Research , or the  Department of Health and Social Care.
Patient Selection This study is a single-center retrospective analysis of all consecutive patients undergoing haplo-HSCT after a TBF conditioning regimen between August 2013 and December 2017 at Saint Antoine Hospital, Paris. Fifty-one patients age 16 years were included. All patients who proceeded to transplantation provided written informed consent for the use of their data for clinical research, in accordance with the modified Declaration of Helsinki and with approval of the local Ethical Committee. Patients were stratified according to the Disease Risk Index (DRI)  [21].
The simulations include samples containing only the B 0 s meson decay channels being measured, and samples with inclusive (prompt and nonprompt) J/ψ mesons. Proton-proton collisions are generated with PYTHIA8 v212  [32]  tune CUETP8M1  [33]  and propagated through the CMS detector using the GEANT4 package  [34]. The decay of the B 0 s mesons is modeled with EVTGEN 1. 3. 0 [35] , and final-state photon radiation in the B 0 s decays is simulated with PHOTOS 2. 0  [36]. For the PbPb MC samples, each PYTHIA8 event is embedded into a PbPb collision event generated with HYDJET 1. 8  [37] , which is tuned to reproduce global event properties, such as the charged-hadron p T spectrum and particle multiplicity. For both samples, the signal p T shape is reweighted to match the one from FONLL. For both pp and PbPb data and MC samples, the dimuon and ditrack mass distributions/resolutions are consistent.
Events were collected with the same trigger during the pp and PbPb data acquisition, requiring the presence of two muon candidates (with no explicit momentum threshold) in coincidence with a bunch crossing. For the offline analysis, events have to pass a set of selection criteria designed to reject events from background processes (beam-gas collisions and beam scraping events) as described in Ref. [38]. Events are required to have at least one reconstructed primary interaction vertex, formed by two or more tracks, with a distance from the center of the nominal interaction region of less than 15 cm along the beam axis. In PbPb collisions, the shapes of the clusters in the pixel detector have to be compatible with those expected from particles produced by a PbPb collision  [39]. In order to select hadronic collisions, the PbPb events are also required to have at least three towers in each of the HF detectors with energy deposits of more than 3 GeV per tower.
Peaking structures that could arise from the background contamination of other B meson decays (e. g. , B 0 → J/ψ K * 0 ) were found to be negligible as a consequence of the tight selection on the mass of the φ candidate. The differential cross section for B 0 s production in |y| < 2. 4 is computed in each p T interval according to dσ B 0 s dp T |y|<2. 4 = 1 2 1 B L 1 ∆p T N (B 0 s +B 0 s ) pp (p T ) α pp (p T ) pp (p T ) |y|<2. 4 , (1) for pp data, and for PbPb data according to 1 T AA dN B 0 s PbPb dp T |y|<2. 4 = 1 2 1 B N MB T AA 1 ∆p T N (B 0 s +B 0 s ) PbPb (p T ) α PbPb (p T ) PbPb (p T ) |y|<2. 4. ( ) 2 The N (B 0 s +B 0 s ) pp,PbPb is the raw signal yield extracted in each p T interval of width ∆p T , (α, ) pp,PbPb represents the corresponding acceptance times efficiency, and B is the branching fraction of the decay chain.
This provides an estimate of the potential difference between different selection criteria. The full difference between the two methods is propagated as a systematic uncertainty. For the PbPb results, because of the small signal in  data, in order to minimize the impact of statistical fluctuations, a different approach was taken. In this case, the B 0 s selection uncertainty was estimated using the pp data sample, as the full difference in the yield between the pp results with the BDT trained on the pp sample (the nominal result) and the results with the BDT trained on the PbPb sample (the selection used for the PbPb results). The bin-by-bin systematic uncertainties associated with the acceptance correction are estimated by varying the shape of the generated B 0 s meson p T and y spectra. For the purpose of the systematic studies only, both data and MC are split into four p T and y bins.
The ratio between data and simulated p T spectra (including their statistical uncertainties) is used to generate pseudoexperiments ('toys'). Each toy is fit with a polynomial, which is then used to reweight the MC B 0 s meson p T spectra. A new acceptance value is calculated for each modified shape, for each kinematic bin. The root mean square (RMS) of all acceptances determined via toys is propagated as the systematic uncertainty by choosing the maximum RMS value emerging from the p T and y shape variations. Because of the small signal available, for the PbPb results the pp ratio is used to generate the toys. There is also an uncertainty assigned to account for potential bias in the efficiency calculations from the FONLL simulations of the B 0 s meson p T shape. This uncertainty is calculated as the difference between the nominal results and those obtained by generating the PYTHIA p T shape.
An additional uncertainty comes from the finite size of the MC samples. This is determined by the statistical uncertainty of the simulated signal, after applying all selection criteria. The uncertainty in the efficiency of the muon trigger, reconstruction, and identification is evaluated bin-by-bin using control samples in data  [44]. A relative systematic uncertainty of 4% per hadron track in pp collisions  [29]  and 6% in PbPb collisions  [38]  is also considered, to account for the uncertainty in the track reconstruction efficiency. This uncertainty propagates to 8% and 12% for the B 0 s measurement in pp and PbPb, respectively. The systematic uncertainty in the cross section measurement is computed as the sum in quadrature of the different contributions mentioned above. The uncertainty in the B 0 s meson decay B is 7.
The difference between the two models below p T ∼ 15 GeV reflects the contribution from recombination processes, which are included in the TAMU but not in the CUJET3. 0 model. The results measured for p T > 7 GeV/c have the power to disentangle the two models, albeit after an increase in precision, which can be achieved with a bigger data sample. [27]  is also shown for comparison. The global systematic uncertainty, represented by the grey box at R AA = 1, comprises the uncertainties in the integrated luminosity measurement and T AA value. Two B 0 s theoretical calculations are also shown for comparison: TAMU  [17, 48]  and CUJET3. 0 [45] [46] [47]. The line width of the theoretical calculation from Refs. [17, 48]  represents the size of its statistical uncertainty. To further quantify the significance of a possible enhancement of the B 0 s /B + ratio in PbPb with respect to pp collisions, the ratio between the B 0 s and the B + R AA is also calculated, canceling the systematic uncertainty sources that are common to both measurements (acceptance, tracking efficiency, and muon-related).
6  In addition, various low-power technologies were standardized for WSN such as IEEE 802. 15. 4  7  covering physical and MAC levels. On top of IEEE 802. 15. 4, ZigBee standard  3  was also proposed as routing and application protocol. Indeed, the common WSN goal is to collect the environmental data and send it to users. Data quit WSN through sink nodes. The sink is a powerful node which allows linking WSN to an external system such as Internet. WSN data could reach sink node through the following methods: local storage, external storage, or in-network storage. Local storage is a data management scheme that stores data in the node producing them. To gather that data, the sink sends a query to the node that stores it. This process has some disadvantages. First, when nodes capture many events, they have to store a lot of data and thus they soon exhaust its scarce resources.
e. 31 protocols are collected and studied). Furthermore, some extra papers were also studied in an effort to propose a research direction for the efficient in-network storage solution in WSN. During the overview process, we targeted four main library databases that cover most of the in-network storage studies in WSN, namely, IEEE Xplore (41% of the studied papers), ScienceDirect (32%), Springer (12%), and ACM Digital Library (10%). Other library databases (5% of the studied papers) are also targeted in this article including SAGE, MDPI, and Wiley. All the cited conferences in this study were published by IEEE Xplore and ACM Digital Library. We focused on papers published over the last two decades, starting from 2003 (i. e. in-network storage mechanism is first introduced in 2003  15  ). Figure  3  gives the valuable information regarding the number of papers, over the years, that are overviewed and discussed in this study.
These quadrants are recursively divided into sub-quadrants, and then the center of each quadrant is used as rendezvous node. Data replication and query transmission are done in the center of quadrants in recursive manner until they intersect. Advantages and drawbacks: Quadtree construction ensures lower communication overhead than other structures discussed above in this section. 38 However, no mechanism is proposed to counter hotspots around the center of each quadrant. DQT: DQT 40 exploits the location information of the top-left and bottom-right corners of the WSN deployment area for quadtree construction. The main properties of the protocol resemble QDD for successive quadrant construction in storage and query processing. Advantages and drawbacks: Compared to QDD, DQT has lower quadrant construction delay, thus improving the data storage process throughout the WSN.
In the following, protocols belonging to each technique are presented. Flooding-based technique. Flooding-based protocols rely on the message broadcasts throughout the entire WSN for delivering data to rendezvous nodes. Since flooding incurs high energy consumption due to excessive radio communication rate as discussed in literature,  19, 44  unstructured proactive flooding-based protocols use mechanisms such as counter-based and probabilistic flooding. These mechanisms attempt to avoid unnecessary broadcasts in WSN (i. e. to avoid broadcast storm problem). Flooding-based storage protocols include DEEP, USEE, MHopC, tinyDSM, CStorage, and CNCDS. Each protocol is presented as follows. DEEP: DEEP 45 uses probabilistic flooding to limit extensive broadcasts in WSN. It aims to uniformly replicate data in the network while having low communication overhead.
Advantages and drawbacks: Using DEEP, the sink visits only a subset of nodes to collect data produced by the entire network. However, the protocol introduces an excessive message redundancy in WSN. 47 The probabilistic flooding mechanism could be improved to be more selective for reducing communication overhead and improving reliability. Moreover, a node in DEEP keeps track of all received messages during its operation,  45  which is impracticable when a lot of messages were flooded. USEE: USEE  47  proposes a uniform data replication protocol for WSN using three mechanisms. First, it uses the counter-based flooding to disseminate each message in the entire network. Then, data are replicated in each node using a probabilistic storing technique similar to DEEP. Finally, to increase the storage capacity of WSN, USEE ensures that the disseminated data are stored once in each neighborhood using a neighbor storage information mechanism.
A message header called NS is used by USEE. When a node stores the data, it broadcasts the message with NS = 1 to inform neighbor nodes, hence reducing the data replication within each neighborhood. Else, NS is set to 0. Advantages and drawbacks: USEE offers good performance in dense WSN, uniformity of data replication, and high storage capacity of WSN. However, USEE suffers from increasing delay as it uses the counter-based flooding: this mechanism is based on random assessment delay (RAD). Instead of immediately broadcasting a message m, a node i adds m to its casting queue during RAD and in parallel counts the number of duplicated/received messages of m from its neighbors. If the counter reaches a preset threshold Cth, it cancels the broadcasting. This mechanism reduces the communication overhead while having a high reliability (i.
e. data reach all the nodes). However, it increases the latency of message treatment in each node which increases the overall storage process delay throughout the network. USEE was compared to DEEP in the work of Mekki et al. 47  They show that USEE and DEEP have similar performance in terms of reliability since they uniformly replicate data throughout the whole network. However, compared to DEEP, USEE offers lowest memory overhead and lowest communication overhead since counter-based flooding ensures less broadcasting rate than probabilistic one used by DEEP. MHopC: MHopC 48 replicates data in WSN using a counter called Hop indicating the number of hops to do before storing the data. Similar to USEE, the source node begins data message dissemination using the counter-based flooding. A message header is used which defines the initial counter value Hop according to the importance level of the information as discussed by Mekki et al.
The sink can thus reconstruct all stored data in the whole WSN using compressive sensing. Advantages and drawbacks: CStorage is fully scalable and distributed since nodes independently make broadcast decisions without using any neighborhood information. Similar to DEEP, CStorage introduces an excessive messages redundancy in WSN as well as the energy consumption. 51 NCDS: Similar to CStorage, CNCDS  13  employs compressive sensing technique. CNCDS exploits the correlation of sensor readings (data to be stored) for further reducing the number of transmissions in WSN. CNCDS is based on three phases: initialization, source node broadcasting, and intermediate node forwarding. First, in the initialization phase, each node multiplies its sensed data by a random coefficient as described in the work of Razzaque and Dobson.
55 In the second phase, each node is randomly selected as a source with probability ps. Then, each selected node broadcasts its data message. Upon receiving the message, neighbor nodes store the carried data and then broadcast the message with probability pr. Similar to DEEP, the broadcasting probability pr is adjusted according to the neighborhood density. The sink can thus collect data from a subset of nodes and reconstruct all sensor readings data in the whole WSN using compressive sensing. Advantages and drawbacks: CNCDS achieves lower energy consumption than CStorage by further reducing the total number of transmissions during the dissemination process. 13 However, CNCDS scheme is only based on the spatial correlation of sensed data. CNCDS could further improve the energy efficiency of WSN using both spatial and temporary correlations.
RW is a process in which a message is transmitted from a node to a randomly chosen neighbor until the end of the RW length as shown in Figure  12  (i. e. RW length = number of hops). When the RW stops in a node, the carried data are stored. This RW process is started multiple times to fully disseminate data in the network where each RW has different length. Advantages and drawbacks: RaWMS ensures a uniform data replication in WSN. The sink requires only visiting any subset of nodes to collect about 90% of the generated data in the whole network. 59 However, this is achieved at the cost of high communication overhead resulting in a short network lifetime (i. e. a high number of messages have to be repeatedly transmitted in order to meet uniform data replication in WSN). 45 RaWMS was evaluated and compared to DEEP and USEE in literature.