bluuebunny/crossref_metadata_2025_split huggingface dataset:
KEMAL ERASLAN’IN ALİ ŞÎR NEVÂYÎ ÜZERİNE YAPTIĞI ÇALIŞMALAR VE NEVÂYÎ ÜZERİNE GÖRÜŞLERİ
Authors: YILMAZ Burcu 
Published: June 2023

Ömrünü ilme adamış bir şahsiyet, Çağatay Türkçesi ve Ali Şîr Nevâyî deyince akla gelen bilim insanlarından biri, Türk diline yaptığı hizmetlerle Türkolojiye büyük katkılar sunan değerli bilim insanı Prof. Dr. Kemal Eraslan’ın Nevâyî üzerine yaptığı çalışmalar ve onun hakkındaki görüşleri bu makalenin konusunu oluşturmaktadır. Kemal Eraslan, Doğu Türkçesinin XV. yüzyıl başından XIX. yüzyılın sonlarına kadar devam eden ve Orta Asya Edebî Türkçesinin dönemlerinden biri olan Çağatay Türkçesi ile ilgili kapsamlı çalışmalar ortaya koymuştur. Çağatay Türkçesinin dilini yapılandırıp geliştiren, hatta Çağatay dili denildiğinde Nevâyî dili olarak anılan bir dönemle eşleşecek kadar Türk diline hizmetleri olan Ali şîr Nevâyî, Kemal Eraslan’ın 52 yıl boyunca yaptığı ilmî çalışmaların önemli bir kısmını oluşturmaktadır. Kemal Eraslan Ali Şîr Nevâyî, Nesâyimü’l-mahabbe min Şemâyimi’l-fütüvve, Metin ve Dil Özellikleri” başlıklı tezi ile 1970 yılında Doktor unvanını aldı. Akademik kariyerin ilk ve en önemli basamaklarından biri olarak görülen doktora tezinin konusunu da oluşturan Kemal Eraslan’ın Ali Şîr Nevâyî çalışmaları, yıllar içerisinde istikrarlı bir şekilde devam etmiştir. Bu çalışmalarının yanı sıra, verdiği yüksek lisans ve doktora tezlerinin danışmanlığı vasıtası ile de Çağatay Türkçesi ve Ali Şîr Nevâyî ile ilgili çalışmalar yapılmasına katkı sunmuştur. Ayrıca Kemal Eraslan’ın Nevâyî ve eserleriyle ilgili çalışmalarında, Nevâyî hakkındaki görüşleri oldukça orijinal ve dikkatli tespitlerdir.
Bu çalışma üç bölümden oluşmaktadır. Birinci bölümde Kemal Eraslan ve çalışma sahası, Çağatay Türkçesi ve Ali Şîr Nevâyî hakkında kısaca bilgi verilmiştir. İkinci bölümde Kemal Eraslan’ın Nevâyî üzerine yaptığı çalışmalar ve bu çalışmaların detayları tanıtılmıştır. Üçüncü bölümde ise Eraslan’ın, Nevâyî ile ilgili çalışmalarından hareketle Nevâyî hakkındaki görüşleri ve bakış açısı irdelenmiştir.
Using machine learning to predict 30-day readmission and reoperation following resection of supratentorial high-grade gliomas: an ACS NSQIP study involving 9418 patients
Authors: Ghaith Abdul Karim , Ghanem Marc , Zamanian Cameron , Bon-Nieves Antonio A. , Bhandarkar Archis , Nathani Karim , Bydon Mohamad , Quiñones-Hinojosa Alfredo 
Published: June 2023

OBJECTIVE
High-grade gliomas (HGGs) are among the rarest yet most aggressive tumor types in neurosurgical practice. In the current literature, few studies have assessed the drivers of early outcomes following resection of these tumors and investigated their association with quality of care. The authors aimed to identify the clinical predictors for 30-day readmission and reoperation following HGG surgery using the American College of Surgeons (ACS) National Surgical Quality Improvement Project (NSQIP) database and sought to create web-based applications predicting each outcome.

METHODS
Using the ACS NSQIP database, the authors conducted a retrospective, multicenter cohort analysis of patients who underwent resection of supratentorial HGGs between January 1, 2016, and December 31, 2020. Demographics and comorbidities were extracted. The primary outcomes were 30-day unplanned readmission and reoperation. A stratified 80:20 split of the available data was carried out. Supervised machine learning algorithms were trained to predict 30-day outcomes.

RESULTS
A total of 9418 patients were included in our cohort. The observed rate of unplanned readmission within 30 days of surgery was 13.0% (n = 1221). In terms of predictors, weight, chronic steroid use, preoperative blood urea nitrogen level, and white blood cell count were associated with a higher risk of readmission. The observed rate of unplanned reoperation within 30 days of surgery was 5.2% (n = 489). In terms of predictors, increased weight, longer operative time, and more days between hospital admission and operation were associated with an increased risk of early reoperation. The random forest algorithm showed the highest predictive performance for early readmission (area under the curve [AUC] = 0.967), while the XGBoost algorithm showed the highest predictive performance for early reoperation (AUC = 0.985). Web-based tools for both outcomes were deployed (https://glioma-readmission.herokuapp.com/ and https://glioma-reoperation.herokuapp.com/).

CONCLUSIONS
In this study, the authors provide the first nationwide analysis for short-term outcomes in patients undergoing resection of supratentorial HGGs. Multiple patient, hospital, and admission factors were associated with readmission and reoperation, confirmed by machine learning predicting patients’ prognosis, leading to better planning preoperatively and subsequently improved personalized patient care.
Immunohistochemical markers predicting long-term recurrence following clival and spinal chordoma resection: a multicenter study
Authors: Ghaith Abdul Karim , Akinduro Oluwaseun O. , Alexander A. Yohan , Goyal Anshit , Bon-Nieves Antonio , de Macedo Filho Leonardo , Otamendi-Lopez Andrea , Nathani Karim Rizwan , Abode-Iyamah Kingsley , Jentoft Mark E. , Bendok Bernard R. , Clarke Michelle J. , Link Michael J. , Van Gompel Jamie J. , Quiñones-Hinojosa Alfredo , Bydon Mohamad 
Published: June 2023

OBJECTIVE
Chordomas are rare tumors from notochordal remnants and account for 1%–4% of all primary bone malignancies, often arising from the clivus and sacrum. Despite margin-negative resection and postoperative radiotherapy, chordomas often recur. Further, immunohistochemical (IHC) markers have not been assessed as predictive of chordoma recurrence. The authors aimed to identify the IHC markers that are predictive of postoperative long-term (≥ 1 year) chordoma recurrence by using trained multiple tree-based machine learning (ML) algorithms.

METHODS
The authors reviewed the records of patients who had undergone treatment for clival and spinal chordomas between January 2017 and June 2021 across the Mayo Clinic enterprise (Minnesota, Florida, and Arizona). Demographics, type of treatment, histopathology, and other relevant clinical factors were abstracted from each patient record. Decision tree and random forest classifiers were trained and tested to predict long-term recurrence based on unseen data using an 80/20 split.

RESULTS
One hundred fifty-one patients diagnosed and treated for chordomas were identified: 58 chordomas of the clivus, 48 chordomas of the mobile spine, and 45 chordomas sacrococcygeal in origin. Patients diagnosed with cervical chordomas were the oldest among all groups (58 ± 14 years, p = 0.009). Most patients were male (n = 91, 60.3%) and White (n = 139, 92.1%). Most patients underwent resection with or without radiation therapy (n = 129, 85.4%). Subtotal resection followed by radiation therapy (n = 51, 33.8%) was the most common treatment modality, followed by gross-total resection then radiation therapy (n = 43, 28.5%). Multivariate analysis showed that S100 and pan-cytokeratin are more likely to predict the increase in the risk of postoperative recurrence (OR 3.67, 95% CI 1.09–12.42, p= 0.03; and OR 3.74, 95% CI 0.05–2.21, p = 0.02, respectively). In the decision tree analysis, a clinical follow-up > 1897 days was found in 37% of encounters and a 90% chance of being classified for recurrence (accuracy = 77%). Random forest analysis (n = 500 trees) showed that patient age, type of surgical treatment, location of tumor, S100, pan-cytokeratin, and EMA are the factors predicting long-term recurrence.

CONCLUSIONS
The IHC and clinicopathological variables combined with tree-based ML tools successfully demonstrated a high capacity to identify recurrence patterns with an accuracy of 77%. S100, pan-cytokeratin, and EMA were the IHC drivers of recurrence. This shows the power of ML algorithms in analyzing and predicting outcomes of rare conditions of a small sample size.
Incorporation of a biparietal narrowing metric to improve the ability of machine learning models to detect sagittal craniosynostosis with 2D photographs
Authors: Anderson Megan G. , Jungbauer Dana , Leclair Nathan K. , Ahn Edward S. , Stoltz Petronella , Martin Jonathan E. , Hersh David S. , Bookland Markus J. 
Published: June 2023

OBJECTIVE
Sagittal craniosynostosis is the most common form of craniosynostosis and typically results in scaphocephaly, which is characterized by biparietal narrowing, compensatory frontal bossing, and an occipital prominence. The cephalic index (CI) is a simple metric for quantifying the degree of cranial narrowing and is often used to diagnose sagittal craniosynostosis. However, patients with variant forms of sagittal craniosynostosis may present with a "normal" CI, depending on the part of the suture that is closed. As machine learning (ML) algorithms are developed to assist in the diagnosis of cranial deformities, metrics that reflect the other phenotypic features of sagittal craniosynostosis are needed. In this study the authors sought to describe the posterior arc angle (PAA), a measurement of biparietal narrowing that is obtained with 2D photographs, and elucidate the role of PAA as an adjuvant to the CI in characterizing scaphocephaly and the potential relevance of PAA in new ML model development.

METHODS
The authors retrospectively reviewed 1013 craniofacial patients treated during the period from 2006 to 2021. Orthogonal top-down photographs were used to calculate the CI and PAA. Distribution densities, receiver operating characteristic (ROC) curves, and chi-square analyses were used to describe the relative predictive utility of each method for sagittal craniosynostosis.

RESULTS
In total, 1001 patients underwent paired CI and PAA measurements and a clinical head shape diagnosis (sagittal craniosynostosis, n = 122; other cranial deformity, n = 565; normocephalic, n = 314). The area under the ROC curve (AUC) for the CI was 98.5% (95% confidence interval 97.8%–99.2%, p < 0.001), with an optimum specificity of 92.6% and sensitivity of 93.4%. The PAA had an AUC of 97.4% (95% confidence interval 96.0%–98.8%, p < 0.001) with an optimum specificity of 94.9% and sensitivity of 90.2%. In 6 of 122 (4.9%) cases of sagittal craniosynostosis, the PAA was abnormal while the CI was normal. This means that adding a PAA cutoff branch to a partition model increases the detection of sagittal craniosynostosis.

CONCLUSIONS
Both CI and PAA are excellent discriminators for sagittal craniosynostosis. Using an accuracy-optimized partition model, the addition of the PAA to the CI increased model sensitivity compared to using the CI alone. Using a model that incorporates both CI and PAA could assist in the early identification and treatment of sagittal craniosynostosis via automated and semiautomated algorithms that utilize tree-based ML models.
Critically reading machine learning literature in neurosurgery: a reader’s guide and checklist for appraising prediction models
Authors: Emani Sivaram , Swaminathan Akshay , Grobman Ben , Duvall Julia B. , Lopez Ivan , Arnaout Omar , Huang Kevin T. 
Published: June 2023

OBJECTIVE
Machine learning (ML) has become an increasingly popular tool for use in neurosurgical research. The number of publications and interest in the field have recently seen significant expansion in both quantity and complexity. However, this also places a commensurate burden on the general neurosurgical readership to appraise this literature and decide if these algorithms can be effectively translated into practice. To this end, the authors sought to review the burgeoning neurosurgical ML literature and to develop a checklist to help readers critically review and digest this work.

METHODS
The authors performed a literature search of recent ML papers in the PubMed database with the terms "neurosurgery" AND "machine learning," with additional modifiers "trauma," "cancer," "pediatric," and "spine" also used to ensure a diverse selection of relevant papers within the field. Papers were reviewed for their ML methodology, including the formulation of the clinical problem, data acquisition, data preprocessing, model development, model validation, model performance, and model deployment.

RESULTS
The resulting checklist consists of 14 key questions for critically appraising ML models and development techniques; these are organized according to their timing along the standard ML workflow. In addition, the authors provide an overview of the ML development process, as well as a review of key terms, models, and concepts referenced in the literature.

CONCLUSIONS
ML is poised to become an increasingly important part of neurosurgical research and clinical care. The authors hope that dissemination of education on ML techniques will help neurosurgeons to critically review new research better and more effectively integrate this technology into their practices.
Interhemispheric connections in the maintenance of language performance and prognosis prediction: fully connected layer-based deep learning model analysis
Authors: Zhang Haosu , Tehlan Kartikay , Ille Sebastian , Schwendner Maximilian , Gong Zhenyu , Schroeder Axel , Meyer Bernhard , Krieg Sandro M. 
Published: June 2023

OBJECTIVE
Language-related networks have been recognized in functional maintenance, which has also been considered the mechanism of plasticity and reorganization in patients with cerebral malignant tumors. However, the role of interhemispheric connections (ICs) in language restoration remains unclear at the network level. Navigated transcranial magnetic stimulation (nTMS) and diffusion tensor imaging fiber tracking data were used to identify language-eloquent regions and their corresponding subcortical structures, respectively.

METHODS
Preoperative image–based IC networks and nTMS mapping data from 30 patients without preoperative and postoperative aphasia as the nonaphasia group, 30 patients with preoperative and postoperative aphasia as the glioma-induced aphasia (GIA) group, and 30 patients without preoperative aphasia but who developed aphasia after the operation as the surgery-related aphasia group were investigated using fully connected layer-based deep learning (FC-DL) analysis to weight ICs.

RESULTS
GIA patients had more weighted ICs than the patients in the other groups. Weighted ICs between the left precuneus and right paracentral lobule, and between the left and right cuneus, were significantly different among these three groups. The FC-DL approach for modeling functional and structural connectivity was also tested for its potential to predict postoperative language levels, and both the achieved sensitivity and specificity were greater than 70%. Weighted IC was reorganized more in GIA patients to compensate for language loss.

CONCLUSIONS
The authors’ method offers a new perspective to investigate brain structural organization and predict functional prognosis.
Evaluating a 3D deep learning pipeline for cerebral vessel and intracranial aneurysm segmentation from computed tomography angiography–digital subtraction angiography image pairs
Authors: Patel Tatsat R. , Patel Aakash , Veeturi Sricharan S. , Shah Munjal , Waqas Muhammad , Monteiro Andre , Baig Ammad A. , Pinter Nandor , Levy Elad I. , Siddiqui Adnan H. , Tutino Vincent M. 
Published: June 2023

OBJECTIVE
Computed tomography angiography (CTA) is the most widely used imaging modality for intracranial aneurysm (IA) management, yet it remains inferior to digital subtraction angiography (DSA) for IA detection, particularly of small IAs in the cavernous carotid region. The authors evaluated a deep learning pipeline for segmentation of vessels and IAs from CTA using coregistered, segmented DSA images as ground truth.

METHODS
Using 50 paired CTA-DSA images, the authors trained (n = 27), validated (n = 3), and tested (n = 20) a deep learning model (3D DeepMedic) for cerebrovasculature segmentation from CTA. A landmark-based coregistration algorithm was used for registration and upsampling of CTA images to paired DSA images. Segmented vessels from the DSA were used as the ground truth. Accuracy of the model for vessel segmentation was evaluated using conventional metrics (dice similarity coefficient [DSC]) and vessel segmentation–specific metrics, like connectivity-area-length (CAL). On the test cases (20 IAs), 3 expert raters attempted to detect and segment IAs. For each rater, the authors recorded the rate of IA detection, and for detected IAs, raters segmented and calculated important IA morphology parameters to quantify the differences in IA segmentation by raters to segmentations by DeepMedic. The agreement between raters, DeepMedic, and ground truth was assessed using Krippendorf’s alpha.

RESULTS
In testing, the DeepMedic model yielded a CAL of 0.971 ± 0.007 and a DSC of 0.868 ± 0.008. The model prediction delineated all IAs and resulted in average error rates of < 10% for all IA morphometrics. Conversely, average IA detection accuracy by the raters was 0.653 (undetected IAs were present to a significantly greater degree on the ICA, likely due to those in the cavernous region, and were significantly smaller). Error rates for IA morphometrics in rater-segmented cases were significantly higher than in DeepMedic-segmented cases, particularly for neck (p = 0.003) and surface area (p = 0.04). For IA morphology, agreement between the raters was acceptable for most metrics, except for the undulation index (α = 0.36) and the nonsphericity index (α = 0.69). Agreement between DeepMedic and ground truth was consistently higher compared with that between expert raters and ground truth.

CONCLUSIONS
This CTA segmentation network (DeepMedic trained on DSA-segmented vessels) provides a high-fidelity solution for CTA vessel segmentation, particularly for vessels and IAs in the carotid cavernous region.
Either you know or you've gotta believe
Authors: Gruber Monika 
Published: June 2023

The standard analysis of knowledge in contemporary epistemology is the view that a subject \(S\) knows a proposition \(p\) iff 1.) \(p\) is true; 2.) \(S\) believes that \(p\); 3.) \(S\) is justified in believing that \(p\). Hence, knowledge is justified true belief (JTB)1. The second condition that (propositional) knowledge entails belief is called the entailment thesis and has been considered by the majority as uncontroversial. Numerous accounts of knowledge and belief have been constructed involving entailment thesis granting it a default status.

The generally accepted view in today's epistemology is that knowledge is something more than 'merely' belief. Few philosophers argued against the entailment thesis, even if barely any arguments for it have been presented. Some of the earlier voices in contemporary epistemology arguing against the entailment thesis and for the superiority of belief were Colin Radford's and Bernard Williams'. Today Blake Myers-Schulz and Eric Schwitzgebel as well as Dylan Murray, Justin Sytsma and Jonathan Livengood provide convincing arguments disputing the rightfulness of the entailment thesis. In what follows I will discuss different accounts of knowledge and belief and point out the overlooked connection between these accounts and argue that it supports yet another line of argument against the entailment thesis. I will further argue that knowledge and belief are two separate concepts, neither of which can be defined in terms of the other, and that it is a belief that is more complex and requires further conditions than knowledge, most of all agent's actions.
Association between Body Image Focused Social Media Usage (BSMU), Resilience, Attachment and Eating-related Problems among High School Students in Bangkok
Authors: Carriker Nicholas , Hongsanguansri Sirichai , Pavasuthipaisit Chosita , Kiatrungrit Komsan 
Published: June 2023

Objective: This study aimed to find the association among body image focused social media usage (BSMU), resilience, attachment, and eating-related problems among Thai adolescents.
Materials and Methods: Cross-sectional descriptive research was conducted with a sample of 495 high school students from three schools in Bangkok. The participants answered an online questionnaire comprised of age, sex, height/weight, BSMU, Body-esteem Scale for Adolescents and Adults, Eating Attitudes Test, Inventory of Parent and Peer Attachment for Children, and the Thai Resilience Quotient. Descriptive statistics were used to analyze demographic information, body satisfaction, resilience, attachment, and eating-related problems. T-tests, chi-square, and multivariate logistic regression analysis were performed to explore the associations between these variables.
Results: Mean (SD) age was 17.06 (0.805), with 307 female participants (62%). Time spent on social media was found to be associated with increased risk of binging (AOR (CI) = 1.71 (1.14-2.56)). BSMU was associated with increased risk of inappropriate eating attitudes, binging, purging and using laxative (AOR (CI) = 1.14 (1.03-1.27), 1.14 (1.06-1.22), 1.20 (1.04-1.40), and 1.21 (1.09-1.34) respectively). Higher resilience was found to associated with lower risk in binging (AOR (CI) = 0.45 (0.21-0.97)). However, attachment is not associated with any of eating-related problems.
Conclusion: BSMU usage was associated with inappropriate eating attitudes and behavior. Findings also suggest that higher resilience and stronger attachment were associated with lower risk of eating-related problems. The effectiveness of resilience and attachment improvement programs should be explored to help protect against eating problems.
Contraceptive Practices and Awareness of Emergency Contraceptive Pills among Rural Women of Reproductive Age
Authors: Lima Fatema Akter , Chowdhury Ayesha Mahzabin , Ghosh Debashish Kumer , Salim Airin , Alam Md Hasibul , Uddin Md Shahin 
Published: June 2023

Background: Regular contraceptive use and emergency contraception are tools to prevent population explosion. Objective: To assess the contraceptive practices and awareness of emergency contraceptive pills among rural women of reproductive age. Methods: The cross sectional study is conducted over 3months (December 2021 to February 2022) among 345 women of reproductive age (15-49 years) who living with their husbands in selected rural residence at Mostafapur village under Sadar Upazilla in Cumilla district. Women who were pregnant, had a child younger than 2 years, or had any physical & psychological disorder were excluded. Participants are selected by convenience sampling technique, data are collected by face to face interview with a semi-structured questionnaire to assess socio-demographic characteristics, contraceptive practices and awareness of emergency contraceptive pills. Data are analysed by SPSS software. Results: In this study, majority (48.7%) of the participants are in the age group 25-34 years. Most of them are housewives (92.2%) and 33.0%has secondary education. The mean (±SD) monthly family income of the participants is 25463.77 (±15971.158) tk and 58.3% of the participants has 1-2 children. About the current practice of contraceptive method, 52.2% of the participants practice and OCP is the widely used method (58.3%), 44.40% take decision by both husband and wife. About reasons for using contraceptives, majority (38.3%) of them use due to spacing of birth and 27.2% cases do not use due to husband disapproval. Regarding awareness of ECPs, 55.9% of them aware of and Norpill is the widely heard method (20.7%). 27.4% of the participants’ source of information is frommedia: TV/Radio. About correct time of taking ECPs, 33.60%of them have no knowledge. Regarding conditions where ECPs are to be taken, majority 39.7% of them have no knowledge. About benefits of taking ECPs, majority (79.30%) of them know it can prevent pregnancy. Conclusion Contraceptive practices and awareness of ECPs are not satisfactory among rural women. So national wide campaign program should be conducted to improve this field.
Central Medical College Journal Vol 6 No 1 January 2022 Page: 22-27