Regional Network Research Articles

The incidence and prevalence of Dupuytren’s disease in primary care: results from a text mining approach on registration data

Background The focus of research and management of Dupuytren’s disease (DD) is shifting from relieving symptoms in the later stages of disease towards the prevention of contractures. Treatment services might likewise shift towards primary care. Studying characteristics of DD patients who seek medical care for the first time, may identify a symptomatic target group for early DD treatments. We present the first study that estimates the incidence and prevalence of DD in primary care by applying a text-mining algorithm to registration data. Methods This is a population-based cohort study using electronic health records from Dutch general practices involved in a regional research network. Descriptive statistics were used to describe sex, age, comorbidities and lifestyle factors, the latter two were identified via International Classification of Primary Care (ICPC) codes. Incidence rate was calculated as number of patients with a first contact for DD/1000 person years for the years 2017–2021, point prevalence as the percentage of patients with a contact for DD in 2021. DD contacts were identified using a text-mining algorithm. Results The incidence ranged between 1.41 and 1.72/1000 person years and the overall prevalence was 1.99%. Incidence and prevalence are higher among males and increase with age, peaking between 61 and 80 years. Conclusions Our results of prevalence and incidence of DD in primary care give an insight into the relevant population of patients with symptomatic DD that might be the future target group for potential disease controlling treatments.

Seismometer Orientation Measurements of Broadband Seismic Stations in the China Digital Seismograph Network

ABSTRACT The China Digital Seismograph Network, one of the largest national seismic networks, has been operating for over four decades which provides valuable seismic data for various scientific studies. Our investigation gathered a comprehensive dataset comprising 5,456,816 three-component waveforms from 3187 seismic events that took place over nine years (2014–2022). We assessed sensor orientations at 1056 broadband stations using the P-wave polarization method. Together with our calculation results, operation and maintenance log of regional networks, on-site checking, and manual inspection, we identified and addressed issues related to temporal changes of orientation, polarity reversal, and channel mislabeling. We found that ∼65.8% of seismometers (694) were well aligned with the absolute misorientation angle ≤3°, 20.8% of seismometers (220) were fairly well aligned with the absolute misorientation angle lying between 3° and 10°, 3.6% (38) of seismometers were misaligned exceeding 10°, and 9.8% of seismometers (104) showed a temporal variation in alignment. The fairly high consistency between our numerical results and gyrocompass measurements confirms the reliability of our investigation. We further compared the results of P-wave receiver functions analysis before and after sensor orientation correction. The findings indicate that sensor misorientation angles may lead to inaccurate and unstable seismological results. Therefore, conducting a systematic assessment, diagnosis, and correction for sensor orientation would be beneficial for advancing seismological analysis by promoting consistency, efficiency, accuracy, collaboration, reproducibility, and adaptability in data processing and interpretation.

Envoy Trade and the End of the Tribute Relationship between Vietnam and China, 1802–1885

ABSTRACT This article analyses the commercial activities between Vietnam and China that were conducted by diplomatic envoys (ambassadors) in the 19th century, focusing on three aspects: those that envoys coordinated during tribute missions or diplomatic visits, the trade that they facilitated in the form of buying and selling goods in the other country, and the commerce that occurred when envoys escorted distressed or expelled travellers back to their home country. The analysis reveals that the trade conducted by ambassadors through most of the century was a two-way commercial exchange and driven by mutual interests. However, China’s demand for foreign goods was significantly less pronounced than that of Vietnam. Furthermore, there was a notable degree of asymmetry in benefits and an unequal level of control and influence in this form of trade. This asymmetry, along with the increasingly diverse forms of exchange between China and other countries (especially the growth of private trade) and the expanding role of Western merchants in the regional trade network, eventually ended the dominance of ambassadorial trade in commercial relations between Vietnam and China by the late 19th century. It also contributed to the end of the centuries-long tributary relationship between the two countries.

Structural resilience disparities in regional city networks under the influence of Typhoon Fitow and COVID-19

The integration of regional city networks plays a pivotal role in the regional development. Assessing and analyzing the structural resilience of these networks holds significance in enhancing regional comprehensive disaster prevention. This study focuses on the Yangtze River Delta, examining its regional city networks encompassing information, economic, transportation, and innovation domains. Utilizing the social network analysis, projection pursuit model based on the genetic algorithm, coupling coordination degree model, coefficient of variation and spatial Gini coefficient, we scrutinized the structural resilience and evolution difference of these networks under different disasters. We found that the structural resilience of economic and transportation networks that depend on infrastructure construction are more limited by geographical distance, and information and innovation networks with weaker physical dependence have stronger structural resilience. The impact of Typhoon Fitow on the structural resilience of individual networks was mainly concentrated in coastal affected cities, while the impact after COVID-19 was mainly in marginal cities. The spatial pattern of the structural resilience of multiple networks shifted from low level equilibrium to agglomeration development. Finally, nodes were identified into three types according to the resilience performance. This research offers a scientific foundation for quantifying regional resilience and formulating network optimization strategies.

Image segmentation for pest detection of crop leaves by improvement of regional convolutional neural network

Pest detection is important for crop cultivation. Crop leaf is the main place of pest invasion. Current technologies to detect crop pests have constraints, such as low efficiency, storage demands and limited precision. Image segmentation is a fast and efficient computer-aided detection technology. High resolution image capture solidly supports the crucial processes in discerning pests from images. Study of analytical methods help parse information in the images. In this paper, a regional convolutional neural network (R-CNN) architecture is designed in combination with the radial bisymmetric divergence (RBD) method for enhancing the efficiency of image segmentation. As a special application of RBD, the hierarchical mask (HM) is produced to endorse detection and classification of the leaf-dwelling pests, offering enhanced efficiency and reduced storage requirements. Moreover, to deal with some mislabeled data, a threshold variable is introduced to adjust a fault-tolerant mechanism into HM, to generate a novel threshold-based hierarchical mask (TbHM). Consequently, the hierarchical mask R-CNN (HM-R-CNN) model and the threshold-based hierarchical mask R-CNN (TbHM-R-CNN) model are established to detect various types of healthy and pest-invasive crop leaves to select the regional image features that are rich in pest information. Then simple linear iterative clustering (SLIC) method is incorporation to finish the image segmentation for the classification of pest invasion. The models are tuned and optimized, then validated. The most optimized modeling results are from the TbHM-R-CNN model, with the classification accuracy of 96.2%, the recall of 97.5% and the F1 score of 0.982. Additionally, the HM-R-CNN model observed appreciable results second only to the best model. These results indicate that the proposed methodologies are well-suited for training and testing a dataset of plant diseases, offering heightened accuracy in pest classification. This study revealed that the proposed methods significantly outperform the existing techniques, marking a substantial improvement over current methods.

An evaluation and innovative coupling of seawater heat pumps in district heating networks

Seawater heat pumps present promising solutions for the decarbonization of the heating sector by utilising seawater as a renewable heat source. This study explores their integration into district heating networks in cold climate regions, focusing on case studies in Estonia, and Norway. A main challenge lies in the freezing threat of seawater during winter, prompting investigations into innovative solutions. This research evaluates two proposed strategies: coupling seawater heat pumps with an additional heat source and locating them far off the shore where water temperatures are more stable. The study employs a multifaceted approach, considering technical, economic, and environmental factors. The research assesses and models seawater heat pumps in two case studies, exploring 10 MW heat supply scenarios. Performance indicators include Coefficient of Performance (COP), Electricity consumption, CO2 emissions, and Levelized Cost of Heat (LCOH). Results highlight the feasibility and advantages of coupling free heat sources with seawater heat pumps in district heating networks. In the Norwegian case, coupling waste heat and seawater heat pumps significantly reduces electricity consumption, CO2 emissions, and LCOH by 88 %, 65 %, and 76 %, respectively. In Tallinn, where seawater freezing is a concern, installing far-off the shore seawater heat pumps maintain year-round operation with a seasonal COP of 3.5 and a 34 % reduction in CO2 emissions compared to electric boiler coupling. Economic assessments suggest the feasibility of integrating far-off the shore seawater heat pumps in low-power price scenarios.

Traffic speed prediction of regional complex road network based on CapsNet and D-BiLSTM

Accurate and efficient short-term traffic prediction is of great significance in the study of regional traffic network. However, the complex and dynamic spatiotemporal correlation of traffic patterns makes the existing methods insufficient in learning traffic evolution in terms of structural depth and prediction scale. Therefore, this paper proposes a deep learning model combining capsule network (CapsNet) and deep bidirectional LSTM (D-BiLSTM). CapsNet is used to identify the spatial topological structure of the road network and extract spatial features, and D-BiLSTM network is integrated. The forward and backward dependencies of traffic states are considered at the same time, and the bidirectional temporal correlation of different historical periods is captured to predict the traffic of large-scale complex road networks in the target area. Experiments on real traffic network speed datasets show that the prediction accuracy of the proposed model is improved by more than 10% on average , which is better than other methods. It has high prediction accuracy and good robustness in traffic prediction of regional complex road networks.

Mathematical Limitations of Gravity Model in Constructing Regional Association Networks: A Case Study

This study evaluates the limitations of gravity models in constructing regional association networks, using China’s interprovincial economic connections as a case study. Comparison between a gravity-model-based simulated network and an actual network reveals significant topological differences. The gravity model overestimates the influence of larger, inward-oriented provinces and fails to accurately represent external connections. Attempts to refine the model with additional variables proved ineffective. Further theoretical analysis attributes these deficiencies to measurement bias from the model’s simplified binary perspective and information loss due to dimensional mismatch between pairwise predictions and complex network structures. These findings underscore the need for cautious application of gravity models and the development of more comprehensive analytical frameworks in regional network analysis.

Pre-Stimulus Activity of Left and Right TPJ in Linguistic Predictive Processing: A MEG Study.

The left and right temporoparietal junctions (TPJs) are two brain areas involved in several brain networks, largely studied for their diverse roles, from attentional orientation to theory of mind and, recently, predictive processing. In predictive processing, one crucial concept is prior precision, that is, the reliability of the predictions of incoming stimuli. This has been linked with modulations of alpha power as measured with electrophysiological techniques, but TPJs have seldom been studied in this framework. The present article investigates, using magnetoencephalography, whether spontaneous oscillations in pre-stimulus alpha power in the left and right TPJs can modulate brain responses during a linguistic task that requires predictive processing in literal and non-literal sentences. Overall, results show that pre-stimulus alpha power in the rTPJ was associated with post-stimulus responses only in the left superior temporal gyrus, while lTPJ pre-stimulus alpha power was associated with post-stimulus activity in Broca's area, left middle temporal gyrus, and left superior temporal gyrus. We conclude that both the right and left TPJs have a role in linguistic prediction, involving a network of core language regions, with differences across brain areas and linguistic conditions that can be parsimoniously explained in the context of predictive processing.

Exploring impacts of electricity tariff on charging infrastructure planning: An activity-based approach

In the past decade, electric vehicles (EVs) have gained popularity for their efficiency and environmental benefits. Advances in battery technology and charging equipment have yielded long-range EVs and fast-charging. However, many major cities lack adequate charging infrastructure for daily EV use. This study addresses this gap by integrating activity-based modeling, charging behavior simulation, and charging infrastructure optimization. The research utilizes the POLARIS agent-based transportation model to accurately capture user activities, trip patterns, and traffic flows. Additionally, the study investigates the impact of fixed and spatiotemporal electricity rate distributions on optimal charging infrastructure deployment. The framework is applied to the Chicago regional area network and analyzed under various EV ownership scenarios. The results reveal significant impacts of the charging pricing strategy on user decision-making and charging demand distribution. There is also a need for consistent pricing policies in charging infrastructure planning and operational phases to avoid drops in service quality.

Efficient Distributed Transfer Learning for Large‐Scale Gaussian Graphic Models

ABSTRACTA transfer learning for the large‐scale Gaussian graphical models (GGMs) is considered in a distributed architecture for fitting these target and auxiliary studies scattered across multiple sites. A distributed transfer learning algorithm, DisPower‐Trans‐CLIME, is proposed to learn the target GGMs by incorporating the data from similar and related auxiliary studies. The algorithm is communication efficient via the distributed power method for matrix decomposition. We show that DisPower‐Trans‐CLIME has a fast convergence rate comparable to the centred Trans‐CLIME algorithm. A debiased DisPower‐Trans‐CLIME is constructed and is proved to be element‐wise asymptotically normal for statistical inference. Thus, a multiple testing procedure is developed to detect edge of GGMs with false discovery rate (FDR) control. Extensive simulation experiments have been conducted to demonstrate superior numerical performance of our proposed learning algorithm on estimation and edge detection. It is also applied to infer the regional connection networks in brain regions of interest (ROIs) based on a target hospital site by leveraging the graph from multiple other hospital sites for autism spectrum disorder. We observe that the degrees of regional connectivity in the right brain are balanced, while the ones of the left brain region are extremely uneven because of the presence of multiple strong connections. However, the specific association, between the degrees of connectivity of these regions and ASD disease, is unknown.

Multi-Scale Candidate Fusion and Optimization-Based 3D Object Detection Algorithm

To address the issues of target omission and the inclusion of a large number of background points in keypoint sampling for point cloud-based object detection, an improved algorithm based on the PV-RCNN network is introduced. This approach employs both a regional proposal fusion network and weighted Non-Maximum Suppression (NMS) to merge proposals generated at various scales while eliminating redundancy. A segmentation network is utilized to segment foreground points from the original point cloud, and object center points are identified based on these proposals. Gaussian density functions are employed for regional density estimation, which assigns different sampling weights to solve the problem of difficult sampling in sparse areas. Experimental evaluations on the KITTI dataset indicate that the algorithm enhances the average precision at medium difficulty levels by 0.39%, 1.31%, and 0.63%for cars, pedestrians, and cyclists, respectively. Generalization experiments were also conducted on the Waymo dataset. The results suggest that the introduced algorithm achieves higher accuracy compared to most of the existing 3D object detection networks.

Interhospital transfer among US veterans admitted to community and veterans affairs hospitals for acute myocardial infarction and ischemic stroke before and during the COVID-19 pandemic.

Veterans Health Administration (VHA) enrollees may use community hospitals for inpatient care and sometimes require transfer to larger community or VHA hospitals. Little is known about interhospital transfer patterns among veterans using community and VHA hospitals or how coronavirus disease 2019 (COVID-19) case surges affected transfer. Retrospective cohort study among veterans age 65+ admitted to community and VHA hospitals for acute myocardial infarction (AMI) or acute ischemic stroke (AIS) during 2018-2021. We examined associations between COVID-19 case density in regional hospital referral networks and the likelihood of transfer. A total of 8373 (23.6%) veterans with AMI and 4630 (13.1%) with AIS were transferred in the prepandemic period. Transfer was especially common for rural veterans (36% with AMI, 20% with AIS). Most transfers (88%) were between community hospitals and 6% from community to VHA. Among AMI patients, transfer was less likely among patients age >90 (relative to age 65-69), those with non-White race/ethnicity, and females. Transfer was more common among patients initially seen in rural hospitals (AMI, odds ratio [OR] = 2.73, 95% confidence interval [CI], 2.90-3.74; AIS, OR = 2.43; 95% CI, 2.24-2.65). During 2020-2021, transfer among AMI patients was less likely during COVID-19 case density surges affecting the admitting hospital's referral network (OR = 0.86; 95% CI, 0.78-0.96 for highest compared with lowest quartile of COVID-19 cases). Interhospital transfer was common for veterans with AMI and AIS, especially among rural veterans. Few transfers were to VHA hospitals. COVID-19 case surges were associated with decreased transfer for veterans with AMI, potentially limiting access to needed care.

Distinct visual processing networks for foveal and peripheral visual fields

Foveal and peripheral vision are two distinct modes of visual processing essential for navigating the world. However, it remains unclear if they engage different neural mechanisms and circuits within the visual attentional system. Here, we trained macaques to perform a free-gaze visual search task using natural face and object stimuli and recorded a large number of 14588 visually responsive units from a broadly distributed network of brain regions involved in visual attentional processing. Foveal and peripheral units had substantially different proportions across brain regions and exhibited systematic differences in encoding visual information and visual attention. The spike-local field potential (LFP) coherence of foveal units was more extensively modulated by both attention and visual selectivity, thus indicating differential engagement of the attention and visual coding network compared to peripheral units. Furthermore, we delineated the interaction and coordination between foveal and peripheral processing for spatial attention and saccade selection. Together, the systematic differences between foveal and peripheral processing provide valuable insights into how the brain processes and integrates visual information from different regions of the visual field.

Multivariate and network lesion mapping reveals distinct architectures of domain-specific post-stroke cognitive impairments

BackgroundThe purpose of this study was to identify patterns of structural disconnection and multivariate lesion-behaviour relationships associated with post-stroke deficits across six commonly impacted cognitive domains: executive function, language, memory, numerical processing, praxis, and visuospatial attention. MethodsStroke survivors (n = 593) completed a brief domain-specific cognitive assessment (the Oxford Cognitive Screen (OCS)) during acute hospitalisation. Network-level and multivariate (sparce canonical correlation) lesion mapping analyses were conducted to identify focal neural correlates and distributed patterns of structural disconnection associated with impairment on each of the 16 OCS measures. ResultsNetwork-level and multivariate lesion mapping analyses identified significant correlates for 12/16 and 10/16 OCS measures, respectively which were largely consistent with correlates reported in past work. Language impairments were reliably localised to network- and voxel-level correlates centred in left fronto-temporal regions. Memory impairments were associated with disconnection in a large network of left hemisphere regions. Number processing deficits were associated with damage to voxels centred in the left insular/opercular cortex, as well as disconnection within the surrounding white matter tracts. Within the domain of attention, different subtypes of visuospatial neglect were linked to distinct but partially overlapping patterns of disconnection and voxel-level damage. Praxis impairment was not linked to any voxel-level regions but was significantly associated with disconnection within the left hemisphere dorsal attention network. ConclusionThese results highlight the utility of routine, domain-specific cognitive assessment and imaging data for theoretically-driven lesion mapping analyses, while providing novel insight into the complex anatomical correlates of common and debilitating post-stroke cognitive impairments.

Analysis of water losses in seven small and medium-sized water distribution networks in the south region of Cameroon (Central Africa)

ABSTRACT This paper analyzes and evaluates water losses in seven small and medium-sized water supply networks (WSNs) in the south region of Cameroon in order to contribute to improving the most effective approaches to reducing water losses in Sub-Saharan Africa's WSNs. Data were obtained from the regional delegation of the Camwater in the south region of Cameroon and analyzed using a variety of indices, including the failure intensity index, the unit coefficient of water loss per kilometer, the real leakage balance index, the non-revenue water basic, and the infrastructure leakage index. Results indicate that all the WSNs analyzed achieved mixed results compared with national data and very weak results compared with international standards. This is partly due to outdated infrastructure, rapid urbanization and urban population expansion, poor operation and maintenance policies, inadequate technical skills and technology, a lack of financial discipline, and political, cultural, and social influences. The results place the cumulative volume of water losses in the seven networks studied during the 5 years at 7.3 million cubic meters, which costs the water utility about 2.4 billion FCFA (or 4.4 million USD).

Optimizing clinical research in a regional cancer center network: A multidepartment survey-based approach.

27 Background: Memorial Sloan Kettering Cancer Center’s (MSKCC) regional care network (RCN) encompasses 6 sites across NY and NJ. Further expansion of clinical research (CR) in the RCN provides an opportunity to bring clinical research closer to patients. The CR RCN leadership group was created to identify and address barriers to research operations in the RCN. Methods: A survey was conducted among Medical, Surgical, Neurologic and Radiation Oncologists at the RCN sites (February 2022-April 2024) to assess perspectives on research-related challenges. The 23-question survey included multiple-choice, open-ended, and ranking questions regarding protocol availability, consenting processes, and staffing support. Results were used to inform interventions to address barriers in the RCN. Results: 92/108 (85%) medical oncologists, 16/31 (52%) radiation oncologists, 7/9 (77%) neurologists and 28/70 (40%) surgeons completed the survey, revealing three key challenges: 1) Limited protocol availability: Only 47% of the time was there a suitable medicine trial available in the RCN. Identified barriers to opening desired medicine protocols (n=23 in total) included trials that were closed to accrual, pending amendments, and sponsor approval issues. 2) Inadequate investigator representation: 278 medicine protocols lacked RCN physicians listed as consenting providers. 3) Insufficient research staff. Only 33% were satisfied with the research support, and 62% of physicians identified lack of clinical research support staff as the biggest barrier to accrual. 71% reported delays of more than one week in screening eligible patients due to insufficient support. Following these results, interventions were implemented across departments to overcome these challenges. Appropriate RCN physicians were added to the 278 protocols, and an analysis of staffing needs increased clinical trial nursing and administrative staff. Therapeutic and non-therapeutic accruals in the Department of Medicine rose by 10.5%, and total research visits increased by 21.4% compared to the previous year. Thirteen of the 23 studies in medicine, that were previously only open at main campus, were opened to accrual in the RCN. This survey highlighted research portfolio gaps in multiple departments that are now being addressed. Conclusions: This multi departmental survey-based approach identified key barriers to CR within MSKCC's RCN, and in turn, informed interventions that increased research activity. These findings offer a potential roadmap for optimizing research in similar networks by enhancing protocol accessibility, addressing staffing needs, and fostering collaboration. Further investigations are ongoing to address additional challenges and improve workflows across departments.

Tropospheric delay estimation and influencing factors analysis of GNSS multi-system

Abstract In this paper, the tropospheric delay parameters are estimated by GNSS regional network solution, and the different factors affecting the estimation of tropospheric delay parameters are analyzed experimentally. The high-precision tropospheric delay correction file (.ztd) provided by IGS and the traditional model estimation of the GNSS regional network are compared to verify the final positioning accuracy. The main factors affecting the estimation of tropospheric delay parameters and the conditions conducive to the estimation of tropospheric delay parameters in the data solution process are obtained. The experimental results show that: (1) GPS has the highest tropospheric delay estimation accuracy in the European region, Galileo is slightly worse than GPS, and BDS has the lowest tropospheric delay estimation accuracy; (2) The tropospheric delay estimation accuracy of post-product in different satellite precision products is better than that of real-time products, and CLKM2 is better than CLK93 in real-time products. With the increase of satellite systems, the accuracy of tropospheric delay estimation is gradually improved. Compared with the GPS single system, the accuracy of the GPS+ Galileo+BDS triple system is improved by about 7.2%, 7.2%, and 7.5%; (3) The tropospheric delay estimation accuracy of the European region is better than that of the Australian region; (4) Based on the BDS whole system data solution, the high-precision tropospheric delay correction file (.ztd ) has better positioning accuracy, and the tropospheric delay estimated by the GNSS observation data through the traditional Saastamoninen model of the regional network can also meet the current high-precision positioning requirements in terms of positioning accuracy. The horizontal direction is better than 2 cm, and the elevation direction is better than 4 cm.

Rodent reproductive behavior among males and females after exposure to endocrine-disrupting chemicals.

Sexual reproduction-from both physiological and behavioral perspectives-is dependent upon appropriate connections between a diverse, hormone-modulated network of neural regions. Importantly, these substrates are regulated by hormones across the lifespan from early development to adulthood, making them targets of endocrine-disrupting chemicals (EDCs). Rodents, such as mice and rats, are invaluable to the characterization of EDCs because of their sex-specific, stereotyped appetitive and consummatory reproductive behaviors. Phthalates, bisphenol A (BPA), and EDC mixtures pose a salient risk to the health of humans, wildlife, and livestock because these synthetic compounds are ubiquitous due to their widespread use in mass production of consumer and industrial goods. This review outlines how the hypothalamic-pituitary-gonadal axis regulates male and female sexual behaviors, and how phthalates and BPA can perturb appetitive and consummatory behaviors and impact neural substrates that modulate reproductive behavior. We will then discuss how to progress toward a clearer understanding of the reproductive and neurobiological changes that occur due to EDC exposure.

Practice variation in induction of labor: A critical document analysis on the contribution of regional protocols.

Despite national guidelines with recommendations on induction of labor (IOL), large variation in the use of this intervention exists between regions in the Netherlands. Guidelines are translated into protocols, which give a contextual description of medical practice provided in a given region. Possibly, protocols developed by regional multidisciplinary maternity care networks (MCNs) contribute to the regional variation in IOL. The aim of this study was to assess the variation between regional protocols and national guidelines regarding recommendations on IOL and the extent to which this contributes to practice variation. We performed a systematic document analysis using the Ready materials, Extract data, Analyze, Distil (READ) approach. National guidelines (n = 4) and regional protocols (n = 18) from six MCNs on topics linked to IOL were assessed between October 2021 and April 2022. An analytical framework was used to extract data for the comparison of regional protocols. Some MCNs followed all the recommendations of national guidelines in their regional protocols, others developed their own recommendations, and for some this varied per topic. When developing their own recommendations, MCNs with a high percentage of IOL added additional risk factors and stricter cut-off values. In contrast, MCNs with a low percentage of IOL added more care options for continuing midwife-led care. No clear relationship was observed between the Appraisal of Guidelines for Research & Evaluation (AGREE) scores of the national guidelines and the extent to which regional protocols complied with the recommendations. The translation of national guidelines to regional protocols seemed arbitrary and not very systematic. To reduce unwarranted practice variation in the use of IOL, guidance is needed to better align regional protocols with national guidelines, while including appropriate contextual factors and allowing women's preferences. Additionally, healthcare providers should be trained in practicing evidence-based medicine instead of using evidence.