Ratio Estimates Research Articles

Estimating the composition ratios of network services carried in mixed traffic

Network performance management (NPM) and quality of service (QoS) guarantee are the key concerns of network operators and service providers/consumers, respectively. The basis for traffic scheduling is one of the vital elements guiding the implementation of both NPM and QoS assurance. Although traffic identification technologies can theoretically be applied to providing basis for traffic scheduling, the computational complexity caused by the flow-by-flow processing makes them unsuitable for actual large-scale high-throughput network scenarios. In this work, we introduce a new scheme to provide a basis for service-oriented NPM and QoS guarantee. The proposed scheme treats the mixed traffic as a whole and estimates the composition ratios of network services carried in mixed traffic, which makes traffic scheduling for NPM and QoS more reasonable and flexible. To estimate the composition ratios, the observed mixed traffic is expressed as a bipartite graph according to the communication relationship. Then, the bipartite graph is further projected to a multi-dimensional hyper-image based on node coding, random walking and embedding mapping. By this way, we convert the service composition ratio estimation into a problem similar to image classification and enable it to be solved by general machine learning methods like convolutional neural networks. The proposed scheme facilitates batch traffic scheduling and can reorganize the distribution of compositions of network services for each link based on given management strategies. Its computational complexity is significantly lower than that of flow-by-flow processing methods. We validate the proposed solution via real network data and evaluate its performance with some benchmark methods based on traffic identification. The experimental results show that the proposed solution can accurately infer the service composition ratios of mixed traffic with low computational complexity.

Multi-granularity learning of explicit geometric constraint and contrast for label-efficient medical image segmentation and differentiable clinical function assessment

Automated segmentation is a challenging task in medical image analysis that usually requires a large amount of manually labeled data. However, most current supervised learning based algorithms suffer from insufficient manual annotations, posing a significant difficulty for accurate and robust segmentation. In addition, most current semi-supervised methods lack explicit representations of geometric structure and semantic information, restricting segmentation accuracy. In this work, we propose a hybrid framework to learn polygon vertices, region masks, and their boundaries in a weakly/semi-supervised manner that significantly advances geometric and semantic representations. Firstly, we propose multi-granularity learning of explicit geometric structure constraints via polygon vertices (PolyV) and pixel-wise region (PixelR) segmentation masks in a semi-supervised manner. Secondly, we propose eliminating boundary ambiguity by using an explicit contrastive objective to learn a discriminative feature space of boundary contours at the pixel level with limited annotations. Thirdly, we exploit the task-specific clinical domain knowledge to differentiate the clinical function assessment end-to-end. The ground truth of clinical function assessment, on the other hand, can serve as auxiliary weak supervision for PolyV and PixelR learning. We evaluate the proposed framework on two tasks, including optic disc (OD) and cup (OC) segmentation along with vertical cup-to-disc ratio (vCDR) estimation in fundus images; left ventricle (LV) segmentation at end-diastolic and end-systolic frames along with ejection fraction (LVEF) estimation in two-dimensional echocardiography images. Experiments on nine large-scale datasets of the two tasks under different label settings demonstrate our model’s superior performance on segmentation and clinical function assessment.

547 ADHD and Burn Injuries: The Hidden Readmission Risk in Pediatric Patients

Abstract Introduction Attention-deficit/hyperactivity disorder (ADHD) is a common neurodevelopmental disorder with the hallmark characteristics of poor impulse control, inattention, hyperactivity, and other cognitive dysfunction; as a consequence, children with ADHD are at an increased risk of burn injury. We hypothesized that pediatric burn patients with ADHD have a higher rate of unplanned readmission compared to patients without a diagnosis of ADHD. Methods We analyzed the Nationwide Readmission Database (01/2018 - 10/2018) to compare 60-days readmission rates in children (< 18 yo) with and without a diagnosis of ADHD who were treated for burn injuries on their index admission. Weighted multiple logistic regression was performed to obtain population estimates of odds ratios (OR), adjusting for patients’ demographics, burn severity and location, comorbidities, socioeconomic status, other psychiatric illnesses, and burn treatment on index admission. Results Our cohort included 1,879 children with burn injury. Of these, 79 (4.2%) had a diagnosis of ADHD. ADHD patients were more likely to be male (86.1% vs 59.8%, p=0.0002 ) and older (11.6 ± 3.7 vs 5.4 ± 5.3 years, p=0.0001). Children with ADHD were more likely to have other psychiatric diagnoses (26.6% vs 3.8%), specifically obsessive compulsive disorder (2.5% vs 0%, p=0.004), and other developmental disorders (15.2% vs 1.6%, p=0.0002). Patients with ADHD were more likely to be readmitted within 60 days of their index admission (15.2% vs. 4.8%, p=0.002). On weighted multiple regression analysis, ADHD had a higher adjusted odds ratio of readmission (OR: 2.66, 95%CI: 1.13-6.29). Conclusions Children with ADHD have a higher adjusted risk of readmission following thermal injury. This is potentially due to higher barriers to compliance with wound care, physical therapy, and scar management. Targeted discharge planning for patients with ADHD, including scheduled short-term follow-up appointments, home nursing assistance, and post-discharge phone calls, may potentially lower readmission risks in this vulnerable population. Applicability of Research to Practice Pediatric patients with ADHD are at higher than average risk for unplanned readmission within 60 days of discharge. Pediatric burn centers have the opportunity to adjust their post-discharge practice with these high-risk patients to decrease this risk. Interventions could include: more frequent clinic follow-up, longer duration with outpatient therapy, and additional coaching for caretakers on how to improve patient compliance.

The Impact of ESG Performance of Energy Enterprises on Stock Value

ESG, as an increasingly important evaluation indicator for today's enterprises, is being widely used. This article aims to study the relationship between ESG performance in the energy industry and stock value. This article takes the data of energy companies listed in the Shanghai and Shenzhen A-share markets from 2015 to 2022 as samples, controls for two variables: energy type and industry profitability, and uses the P/E ratio estimation method for analysis. Research has found that energy companies with environmental friendliness, high social reputation, and good management practices have higher stock prices, making them more likely to be favored by investors. The relevant research in this article can effectively help investors optimize their decision-making by analyzing the ESG situation when choosing the energy industry. At the same time, this study can provide some development insights for new and traditional energy enterprises.

A machine learning approach for protected species bycatch estimation

IntroductionMonitoring bycatch of protected species is a fisheries management priority. In practice, protected species bycatch is difficult to precisely or accurately estimate with commonly used ratio estimators or parametric, linear model-based methods. Machine-learning algorithms have been proposed as means of overcoming some of the analytical hurdles in estimating protected species bycatch.MethodsUsing 17 years of set-specific bycatch data derived from 100% observer coverage of the Hawaii shallow-set longline fishery and 25 aligned environmental predictors, we evaluated a new approach for protected species bycatch estimation using Ensemble Random Forests (ERFs). We tested the ability of ERFs to predict interactions with five protected species with varying levels of bycatch in the fishery and methods for correcting these predictions using Type I and Type II error rates from the training data. We also assessed the amount of training data needed to inform a ERF approach by mimicking the sequential addition of new data in each subsequent fishing year.ResultsWe showed that ERF bycatch estimation was most effective for species with greater than 2% interaction rates and error correction improved bycatch estimates for all species but introduced a tendency to regress estimates towards mean rates in the training data. Training data needs differed among species but those above 2% interaction rates required 7-12 years of bycatch data.DiscussionOur machine learning approach can improve bycatch estimates for rare species but comparisons are needed to other approaches to assess which methods perform best for hyperrare species.

Active and passive smoking and breast cancer in Japan: a pooled analysis of nine population-based cohort studies.

Epidemiological studies have shown inconsistent results regarding the link between smoking and breast cancer risk, despite the biological plausibility of a positive association. Participants were 166 611 women from nine prospective cohort studies in Japan which launched in 1984-1994 and followed for 8-22 years. Information on smoking and secondhand smoke was obtained through self-administered baseline questionnaires. Breast cancer was defined as code C50 according to the International Classification of Diseases for Oncology, 3rd Edition or the International Classification of Diseases, 10th Revision. After adjusting for several potential confounders, relative risks for breast cancer were calculated in the individual studies according to the current or previous status of active and passive smoking using Cox regression, followed by a summary estimate of hazard ratios using random-effects meta-analyses. Of the 60 441 participants who reported being premenopausal and 106 170 who reported being postmenopausal at baseline, 897 and 1168 developed breast cancer during follow-up, respectively. Compared with never smokers, current smokers had a higher risk of developing breast cancer before the age of 50 years. In addition, ever smokers who started smoking at 30 years of age or younger, or who started smoking before first childbirth, had a higher risk of developing breast cancer before the age of 50 years. No association between adulthood or childhood exposure to secondhand smoke and breast cancer was observed. Smoking may increase the risk of premenopausal breast cancer, and smoking earlier in life might be especially harmful. The impact of secondhand smoke needs further investigation.

Meta-Analysis of Association of SNP 19 In-del Polymorphism at the CAPN 10 Gene with Type2 Diabetes Mellitus

The CAPN 10 gene encoding the larger subunit of the ubiquitously expressing calpain protein has been found to play a role in the incidence of Type2 diabetes mellitus (T2DM). Several intronic polymorphisms of the CAPN10 gene and the linked haplotypic combinations have been implicated in T2DM. The SNP 19 locus of the gene lying within intron 6 exhibits an in-del polymorphism and has been found to be associated with T2DM in several studies. However, the results have not been constant. Keeping this in view, the present work was carried out to understand the association of the intronic SNP 19 in-del polymorphism at the CAPN 10 gene with T2DM through meta-analysis of the case-control studies. All statistical tests based on PRISMA and PROSPERO were performed on R studio (4.2.3). However, the pooled odds ratio (OR) estimates did not reveal any significant association between polymorphism at the SNP 19 locus of CAPN 10 gene and T2DM. Therefore, it is concluded that polymorphism at the SNP19 locus of CAPN 10 gene alone has no role to play in the etiology of T2DM.

SIDE-real: Supernova Ia Dust Extinction with truncated marginal neural ratio estimation applied to real data

Abstract We present the first fully simulation-based hierarchical analysis of the light curves of a population of low-redshift type Ia supernov aelig; (SNaelig; Ia). Our hardware-accelerated forward model, released in the Python package slicsim, includes stochastic variations of each SN’s spectral flux distribution (based on the pre-trained BayeSN model), extinction from dust in the host and in the Milky Way, redshift, and realistic instrumental noise. By utilising truncated marginal neural ratio estimation (TMNRE), a neural network-enabled simulation-based inference technique, we implicitly marginalise over 4000 latent variables (for a set of ≈ 100 SNaelig; Ia to efficiently infer SN Ia absolute magnitudes and host-galaxy dust properties at the population level while also constraining the parameters of individual objects. Amortisation of the inference procedure allows us to obtain coverage guarantees for our results through Bayesian validation and frequentist calibration. Furthermore, we show a detailed comparison to full likelihood-based inference, implemented through Hamiltonian Monte Carlo, on simulated data and then apply TMNRE to the light curves of 86 SNaelig; Ia from the Carnegie Supernova Project,, deriving marginal posteriors in excellent agreement with previous work. Given its ability to accommodate arbitrarily complex extensions to the forward model—e.g. different populations based on host properties, redshift evolution, complicated photometric redshift estimates, selection effects, and non-la contamination—without significant modifications to the inference procedure, TMNRE has the potential to become the tool of choice for cosmological parameter inference from future, large SN Ia samples.

Impact of covariate model building methods on their clinical relevance evaluation in population pharmacokinetic analyses: comparison of the full model, stepwise covariate model (SCM) and SCM+ approaches.

Covariate analysis in population pharmacokinetics is key for adjusting doses for patients. The main objective of this work was to compare the adequacy of various modeling approaches on covariate clinical relevance decision-making. The full model, stepwise covariate model (SCM) and SCM+ PsN algorithms were compared in a clinical trial simulation of a 383-patient population pharmacokinetic study mixing rich and sparse designs. A one-compartment model with first-order absorption was used. A base model including a body weight effect on CL/F and V/F and a covariate model including 4 additional covariates-parameters relationships were simulated. As for forest plots, ratios between covariates at a specific value and that of a typical individual were calculated with their 90% confidence interval (CI90) using standard errors. Covariates on CL, V and KA were considered relevant if their CI90 fell completely outside the reference area [0.8-1.2]. All approaches provided unbiased covariate ratio estimates. For covariates with a simulated effect, the 3 approaches correctly identify their clinical relevance. However, significant covariates were missed in up to 15% of cases with SCM/SCM+. For covariate with no simulated effects, the full model mainly identified them as non-relevant or with insufficient information while SCM/SCM+ mainly did not select them. SCM/SCM+ assume that non-selected covariates are non-relevant when it could be due to insufficient information, whereas the full model does not make this assumption and is faster. This study must be extended to other methods and completed by a more complex high-dimensional simulation framework.

Estimation of probable maximum aspect ratio of MnS inclusions in non-quenched and tempered steel after isothermal compression

Estimation of probable maximum aspect ratio of MnS inclusions in non-quenched and tempered steel after isothermal compression

Design of an Iterative Method for Enhanced Retinal Image Analysis Using Stacked Deep Learning Operations

The necessity for advanced diagnostic techniques in ophthalmology has become increasingly evident, particularly for conditions such as glaucoma and diabetic retinopathy, which necessitate precise retinal image analysis. Current methodologies, while effective to a degree, fall short in terms of accuracy, efficiency, and adaptability, especially in semantic segmentation and disease classification tasks. These limitations underscore the need for more sophisticated and reliable approaches to retinal image analysis. In response, this work introduces a comprehensive framework leveraging Fully Convolutional Neural Networks (FCNNs) for optic disc segmentation and Cup-to-Disc Ratio (CDR) estimation. The selection of FCNNs is predicated on their demonstrated proficiency in semantic segmentation, ability to discern spatial dependencies, and capacity to learn intricate structures within fundus images without relying on manually engineered features. This approach aims to surpass existing segmentation accuracy benchmarks, targeting over 95% Intersection over Union (IoU) while reducing the mean absolute error in CDR estimation to below 10%. To address the challenges of data variability and quality, the framework incorporates Contrast Limited Adaptive Histogram Equalization (CLAHE) for synthetic data generation, enhancing local contrast while preserving image integrity levels. This method is expected to improve contrast in augmented images by 30%, simultaneously enhancing image quality metrics. Furthermore, the introduction of an overlapping sliding window technique with adaptive patch sizes ensures meticulous coverage and analysis of fundus images, significantly elevating lesion detection sensitivity and reducing false positives. Lastly, the framework employs an innovative multi-disease classification strategy utilizing ensemble learning and stacking of CNN architectures. This synergistic approach amalgamates multiple base models to diminish overfitting risks and enhance generalization capabilities, aiming for a classification accuracy surpassing 95% in binary assessments and 85% for specific retinal diseases. The proposed model not only addresses the current gaps in retinal image analysis but also sets a new standard for precision, reliability, and efficiency in diagnosing and managing ocular diseases, marking a significant step forward in the application of artificial intelligence in ophthalmology.

Ratio estimator for double sampling procedure with non-response: An empirical study

This study proposes a ratio-type estimator for population mean estimation using auxiliary variables with double sampling in the presence of non-response. The study provides expressions for the constant, bias, and mean square errors (MSE) of the proposed estimator and compares it with ten existing estimators. The study employed the secondary source of data collection to evaluate the efficiency of the proposed and existing estimators by analyzing five natural populations from three different sources. The performance of ten (10) estimators was considered in this study. The findings suggest that the proposed estimator and the H estimator provide more accurate and precise estimates of the population mean using an auxiliary variable. Additionally, the study found significant differences amongst the mean values of the constant and bias for the different estimators. A Dunn Kruskal-Wallis multiple comparison tests with the Bonferroni method was performed to ascertain the pair of estimators that contributed to the significant difference observed. When estimating the population means using an auxiliary variable, the proposed estimator outperformed other existing estimators that were taken into consideration in the study

Improvement in variance estimation using transformed auxiliary variable under simple random sampling

This paper offers a novel approach to formulate efficient ratio estimator of the population variance using a transformed auxiliary variable. The impact of transformation on auxiliary information has also been discussed. It is observed that incorporating a transformed auxiliary variable result in a high gain in efficiency. Theoretical properties of the newly developed estimators have been derived. The empirical and simulation studies show that the suggested estimators outperformed the existing estimators.

B→D*ℓνℓ semileptonic form factors from lattice QCD with Möbius domain-wall quarks

We calculate the form factors for the B→D*ℓνℓ decay in 2+1 flavor lattice QCD. For all quark flavors, we employ the Möbius domain-wall action, which preserves chiral symmetry to a good precision. Our gauge ensembles are generated at three lattice cutoffs a−1∼2.5, 3.6, and 4.5 GeV with pion masses as low as Mπ∼230 MeV. The physical lattice size L satisfies the condition MπL≥4 to control finite volume effects (FVEs), while we simulate a smaller size at the smallest Mπ to directly examine FVEs. The bottom quark masses are chosen in a range from the physical charm quark mass to 0.7a−1 to control discretization effects. We extrapolate the form factors to the continuum limit and physical quark masses based on heavy meson chiral perturbation theory at next-to-leading order. Then the recoil parameter dependence is parametrized using a model independent form leading to our estimate of the decay rate ratio between the tau (ℓ=τ) and light lepton (ℓ=e, μ) channels R(D*)=0.252(22) in the Standard Model. A simultaneous fit with recent data from the Belle experiment yields |Vcb|=39.19(91)×10−3, which is consistent with previous exclusive determinations, and shows good consistency in the kinematical distribution of the differential decay rate between the lattice and experimental data. Published by the American Physical Society 2024

Estimation of Proton Stopping Power Ratio and Mean Excitation Energy Using Electron Density and Its Applications via Machine Learning Approach.

The purpose of this study was to develop a simple flexible method for accurate estimation of stopping power ratio (SPR) and mean excitation energy (I) using relative electron density (ρ e). The model was formulated using empirical relationships between SPR, mean excitation energy I, and relative electron density. Some examples were implemented, and a comparison was carried out using other existing methods. The needed coefficients in the model were estimated using optimization tools. Basis vector method (BVM) and Hunemohr and Saito (H-S) method were applied to estimate the ρ e used in the application section. 80 kVp and 150 kVpSn were used as low and high energy, respectively, for the implementation of dual-energy methods. All the examples of the proposed method considered have modeling error that is ≤0.32% and testing root mean square error (RMSE) ≤0.92% for SPR with a mean error close to 0.00%. The method was able to achieve modeling RMSE of 2.12% for mean excitation energy with room for improvement. Similar or better results were achieved in application to BVM. The method showed robustness in application by achieving lower testing error than other presented methods in most cases. It achieved accurate estimation which can be improved using the machine learning algorithm since it is flexible to implement in terms of the function (model) degree and tissue classification.

Neutrosophic robust ratio type estimator for estimating finite population mean

Various authors have put their sincere efforts into proposing ratio estimators for estimating the population's mean and variance under different situations and sampling methods. But the problem arises when data is unstable, imprecise, ambiguous, incomplete and vague. In such situations, classical methods of estimation do not yield precise results, as these methods are not meant for such problems. Given this difficulty, Neutrosophic statistics are the only alternative as it deals with indeterminacy. So in this study, we have proposed a generalized Neutrosophic robust ratio type estimator which can be used to provide good results in such situations, as well as in the case of the presence of outliers. For the evaluation point of view, we have made use of real data set and simulation study to check the efficacy of our suggested estimators over the mentioned existed estimators.

Poorer Outcomes in Bladder Cancer Patients With Diabetes: A Systematic Review and Meta-analysis Addressing Over 226,472 Bladder Cancer Patients

Purpose: Diabetes mellitus, a frequent comorbid condition in cancer patients, has been shown to increase risk of all-site cancer mortality. This relationship has not been systematically studied in bladder cancer patients. This systematic review and meta-analysis aimed to identify, evaluate, and synthesize available evidence on the relationship between history of diabetes and outcomes in bladder cancer patients.Materials and Methods: Systematic searches interrogated OVID MEDLINE, Embase, Web of Science, Google Scholar, and Cochrane Library to identify scholarly reports relating diabetes to all-cause mortality, bladder cancer-specific mortality, recurrence, and progression in bladder cancer patients. After critical review, meta-analysis was used to quantitively synthesize qualifying data and assess potential influence of publication bias, clinical heterogeneity, and residual confounding.Results: We synthesized data on over 226,472 patients treated with curative intent uniquely represented in 28 studies that met quality metrics. Having diabetes was positively associated with each outcome. Hazard ratio estimates were indistinguishable for mortality from any cause, 1.22 (95% confidence interval [CI], 1.12–1.33) and bladder cancer-specific mortality, 1.28 (95% CI 1.17–1.41) and notably stronger in patients with muscle-invasive and high-risk non–muscle-invasive bladder cancer, 1.32 (95% CI, 1.15–1.50) and 1.48 (95% CI, 1.06–2.06). Neither publication bias, systematic error, nor confounding by factors such as smoking or obesity is likely to explain the observed associations.Conclusions: Bladder cancer patients with diabetes experience elevated mortality that is not explained by diabetes-related comorbidities or complications. Future research should explore type, severity, and duration of diabetes in relation to unfavorable bladder cancer outcomes.

Expanded Signal to Noise Ratio Estimates for Validating Next-Generation Satellite Sensors in Oceanic, Coastal, and Inland Waters

The launch of the NASA Plankton, Aerosol, Cloud, ocean Ecosystem (PACE) and the Surface Biology and Geology (SBG) satellite sensors will provide increased spectral resolution compared to existing platforms. These new sensors will require robust calibration and validation datasets, but existing field-based instrumentation is limited in its availability and potential for geographic coverage, particularly for coastal and inland waters, where optical complexity is substantially greater than in the open ocean. The minimum signal-to-noise ratio (SNR) is an important metric for assessing the reliability of derived biogeochemical products and their subsequent use as proxies, such as for biomass, in aquatic systems. The SNR can provide insight into whether legacy sensors can be used for algorithm development as well as calibration and validation activities for next-generation platforms. We extend our previous evaluation of SNR and associated uncertainties for representative coastal and inland targets to include the imaging sensors PRISM and AVIRIS-NG, the airborne-deployed C-AIR radiometers, and the shipboard HydroRad and HyperSAS radiometers, which were not included in the original analysis. Nearly all the assessed hyperspectral sensors fail to meet proposed criteria for SNR or uncertainty in remote sensing reflectance (Rrs) for some part of the spectrum, with the most common failures (>20% uncertainty) below 400 nm, but all the sensors were below the proposed 17.5% uncertainty for derived chlorophyll-a. Instrument suites for both in-water and airborne platforms that are capable of exceeding all the proposed thresholds for SNR and Rrs uncertainty are commercially available. Thus, there is a straightforward path to obtaining calibration and validation data for current and next-generation sensors, but the availability of suitable high spectral resolution sensors is limited.

Prognostic impact of positive microscopic margins (R1 resection) in patients with GIST (gastrointestinal stromal tumours): Results of a multicenter European study

BackgroundAlthough several prognostic factors in GIST have been well studied such as tumour size, mitotic rate, or localization, the influence of microscopic margins or R1 resection remains controversial. The aim of this study was to evaluate the influence of R1 resection on the prognosis of GIST in a large multicentre retrospective series of patients. MethodsFrom 2001 to 2013, 1413 patients who underwent surgery for any site of GIST were identified from 61 European centers. 1098 patients were included, excluding synchronous metastases, concurrent malignancies, R2 resection or GIST recurrence. Tumour rupture (TR) was reclassified according to the Oslo sarcoma classification. Cox proportional hazards ratio and Kaplan-Meier survival estimates were used to analyse 5-year recurrence-free survival (RFS). ResultsOf 1098 patients, 38 (3%) underwent R1 resection with a risk of TR of 11%. The 5-year RFS was 89.6% with a median follow-up of 81 months [range: 31.2–152 months]. On univariate analysis, lower RFS was significantly associated with R1 resection [HR = 2.13; p = 0.04], high risk score according to the modified NIH classification, administration of adjuvant therapy [HR = 2.24; p < 0.001] and intraoperative complications [HR = 2.82; p < 0.001]. Only intraoperative complications [HR = 1.79; p = 0.02] and high risk according to the modified NIH classification including the updated definition of TR [HR = 3.43; p = 0.04] remained significant on multivariate analysis. ConclusionThis study shows that positive microscopic margins are not an independent predictive factor for RFS in GIST when taking into account the up-dated classification of TR. R1 resection may be considered a reasonable alternative to avoid major functional sequelae and should not lead to reoperation.

Prevalence of obstructive sleep apnoea syndrome in oncological patients: A systematic review and meta-analysis.

Obstructive sleep apnoea syndrome (OSAS) has garnered increasing attention in recent years due to its potential association with cancer. This systematic review and meta-analysis aimed to assess the prevalence of OSAS in cancer patients through a comprehensive analysis of existing literature. This systematic review and meta-analysis, conducted in accordance with the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) protocol, aimed to evaluate the prevalence of OSAS in cancer patients. A comprehensive search of electronic databases was performed to identify relevant studies published up to September 2021. Eligible studies reporting on the prevalence of OSAS in cancer patients, encompassing various study designs, were included in the analysis. Pooled estimates of the odds ratios (OR) with 95% confidence intervals (CIs) were calculated using a random effects model. Heterogeneity among the studies was assessed using the I2 statistic. Seventeen studies fulfilled the inclusion criteria and were subsequently included in the review. The prevalence of OSAS in cancer patients was estimated to have an overall OR of 0.80 (95% CI: 0.75-0.85). In comparison with non-cancer patients, cancer patients had a statistically significant greater risk of OSAS, as indicated by the total estimated RR for the prevalence of OSAS in cancer patients, which was 0.89 (95% CI: 0.86-0.92). Nonetheless, there was a significant amount of heterogeneity (I2 = 96%) among the studies. The overall data analysed in this review indicates that patients with cancer had far reduced likelihood of developing OSAS than individuals without cancer. However, the significant variation across the included studies highlights the need for additional investigation to fully clarify the complex association between OSAS and cancer incidence. These results emphasise how critical it is to identify OSAS as a possible comorbidity in cancer patients, one that should be taken into account for clinical management and ongoing research.