Ratio Estimates Research Articles

Non-symbolic estimation of big and small ratios with accurate and noisy feedback

The ratio of two magnitudes can take one of two values depending on the order they are operated on: a ‘big’ ratio of the larger to smaller magnitude, or a ‘small’ ratio of the smaller to larger. Although big and small ratio scales have different metric properties and carry divergent predictions for perceptual comparison tasks, no psychophysical studies have directly compared them. Two experiments are reported in which subjects implicitly learned to compare pairs of brightnesses and line lengths by non-symbolic feedback based on the scaled big ratio, small ratio or difference of the magnitudes presented. Results of Experiment 1 showed all three operations were learned quickly and estimated with a high degree of accuracy that did not significantly differ across groups or between intensive and extensive modalities, though regressions on individual data suggested an overall predisposition towards differences. Experiment 2 tested whether subjects learned to estimate the operation trained or to associate stimulus pairs with correct responses. For each operation, Gaussian noise was added to the feedback that was constant for repetitions of each pair. For all subjects, coefficients for the added noise component were negative when entered in a regression model alongside the trained differences or ratios, and were statistically significant in 80% of individual cases. Thus, subjects learned to estimate the comparative operations and effectively ignored or suppressed the added noise. These results suggest the perceptual system is highly flexible in its capacity for non-symbolic computation, which may reflect a deeper connection between perceptual structure and mathematics.

Characterization of chronic pain, pain interference, and daily pain experiences in adult survivors of childhood cancer: a report from the Childhood Cancer Survivor Study.

Although survivors of childhood cancer are at an increased risk, little is known about the prevalence of chronic pain, associated interference, and daily pain experiences. Survivors (N = 233; mean age = 40.8 years, range 22-64 years; mean time since diagnosis = 32.7 years) from the Childhood Cancer Survivor Study completed pain and psychosocial measures. Survivors with chronic pain completed 2-week, daily measures assessing pain and psychological symptoms using mHealth-based ecological momentary assessment. Multivariable-modified Poisson and linear regression models estimated prevalence ratio estimates (PR) and mean effects with 95% confidence intervals (CI) for associations of key risk factors with chronic pain and pain interference, respectively. Multilevel mixed models examined outcomes of daily pain and pain interference with prior day symptoms. Ninety-six survivors (41%) reported chronic pain, of whom 23 (24%) had severe interference. Chronic pain was associated with previous intravenous methotrexate treatment (PR = 1.6, 95% CI 1.1-2.3), respiratory (PR = 1.8, 95% CI 1.2-2.5), gastrointestinal (PR = 1.6, 95% CI 11.0-2.3), and neurological (PR = 1.5, 95% CI 1.0-2.1) chronic health conditions, unemployment (PR = 1.4, 95% CI 1.0-1.9) and clinically significant depression and anxiety (PR = 2.9, 95% CI 2.0-4.2), as well as a diagnosis of childhood Ewing sarcoma or osteosarcoma (PR = 1.9, 95% CI 1.0-3.5). Higher pain interference was associated with cardiovascular and neurological conditions, unemployment and clinical levels of depression and/or anxiety, and fear of cancer recurrence. For male, but not female survivors, low sleep quality, elevated anxiety, and elevated depression predicted high pain intensity and interference the next day. A substantial proportion of childhood cancer survivors experience chronic pain and significant associated interference. Chronic pain should be routinely evaluated, and interventions are needed.

Optimizing heat transport in a permeable cavity with an isothermal solid block: Influence of nanoparticles volume fraction and wall velocity ratio

Abstract This study examines the influence of wall velocity ratio on mixed convective heat transport in a permeable cavity containing an isothermal solid block at its center. The analysis considers the characteristics of various flow variables, i.e., Darcy number, wall velocity ratio, Richardson number, and volume fraction of suspended nanoparticles, on heat transport and material flow characteristics. The principal equations are solved implementing the semi-implicit method for pressure linked equations algorithm, and the outcomes are compared with existing literature. The study shows that rising estimations of Darcy number, velocity ratio, Richardson number, and nanoparticles volume fraction lead to improved heat transfer rates. For example, at high Richardson number (100) and solid volume fraction (0.05), increasing the velocity ratio from 0.5 to 1.5 results in a 6% (5%) upsurge in heat transport rate. Conversely, at smaller Richardson number (0.01), the heat transport rate upsurges by 29% (28%). Similarly, at high Darcy numbers and low wall velocity ratios, a 3% (4%) escalate in heat transport rate is observed with an increase in nanoparticles concentration from 0 to 0.05, while a 9% (8%) increase in thermal performance is achieved at low Darcy numbers. The study emphasizes the importance of optimizing the combination of nanoparticles volume fraction, Darcy number, velocity ratio, and Richardson number to maximize thermal performance in the porous cavity.

A simple Cox approach to estimating risk ratios without sharing individual-level data in multi-site studies.

Epidemiologic studies frequently use risk ratios to quantify associations between exposures and binary outcomes. When the data are physically stored at multiple data partners, it can be challenging to perform individual-level analysis if data cannot be pooled centrally due to privacy constraints. Existing methods either require multiple file transfers between each data partner and an analysis center (e.g., distributed regression) or only provide approximate estimation of the risk ratio (e.g., meta-analysis). Here we develop a practical method that requires a single transfer of eight summary-level quantities from each data partner. Our approach leverages an existing risk-set method and software originally developed for Cox regression. Sharing only summary-level information, the proposed method provides risk ratio estimates and confidence intervals identical to those that would be provided - if individual-level data were pooled - by the modified Poisson regression. We justify the method theoretically, confirm its performance using simulated data, and implement it in a distributed analysis of COVID-19 data from the U.S. Food and Drug Administration's Sentinel System.

A Class of Ratio and Product Types Estimators of Mean of a Sensitive Variable Using Ranked Set Sampling

A Class of Ratio and Product Types Estimators of Mean of a Sensitive Variable Using Ranked Set Sampling

Discovering 3D hidden elasticity in isotropic and transversely isotropic materials with physics-informed UNets

Three-dimensional variation in structural components or fiber alignments results in complex mechanical property distribution in tissues and biomaterials. In this paper, we use a physics-informed UNet-based neural network model (El-UNet) to discover the three-dimensional (3D) internal composition and space-dependent material properties of heterogeneous isotropic and transversely isotropic materials without a priori knowledge of the composition. We then show the capabilities of El-UNet by validating against data obtained from finite-element simulations of two soft tissues, namely, brain tissue and articular cartilage, under various loading conditions. We first simulated compressive loading of 3D brain tissue comprising of distinct white matter and gray matter mechanical properties undergoing small strains with isotropic linear elastic behavior, where El-UNet reached mean absolute relative errors under 1.5 % for elastic modulus and Poisson's ratio estimations across the 3D volume. We showed that the 3D solution achieved by El-UNet was superior to relative stiffness mapping by inverse of axial strain and two-dimensional plane stress/plane strain approximations. Additionally, we simulated a transversely isotropic articular cartilage with known fiber orientations undergoing compressive loading, and accurately estimated the spatial distribution of all five material parameters, with mean absolute relative errors under 5 %. Our work demonstrates the application of the computationally efficient physics-informed El-UNet in 3D elasticity imaging and provides methods for translation to experimental 3D characterization of soft tissues and other materials. The proposed El-UNet offers a powerful tool for both in vitro and ex vivo tissue analysis, with potential extensions to in vivo diagnostics. Statement of significanceElasticity imaging is a technique that reconstructs mechanical properties of tissue using deformation and force measurements. Given the complexity of this reconstruction, most existing methods have mostly focused on 2D problems. Our work is the first implementation of physics-informed UNets to reconstruct three-dimensional material parameter distributions for isotropic and transversely isotropic linear elastic materials by having deformation and force measurements. We comprehensively validate our model using synthetic data generated using finite element models of biological tissues with high bio-fidelity–the brain and articular cartilage. Our method can be implemented in elasticity imaging scenarios for in vitro and ex vivo mechanical characterization of biomaterials and biological tissues, with potential extensions to in vivo diagnostics.

Understanding stunting and its determinant among children in the most populous state of India using estimation of population mean under ranked set sampling in the presence of missing data

ABSTRACT In real-life scenarios, encountering data with missing values is common, and if not managed carefully from the outset of a study, it can lead to significant biases in survey estimates. Various methods exist for imputing missing values in sampling procedures. Ranked set sampling (RSS) is widely recognized for its superior efficiency compared to simple random sampling. However, limited research has been conducted on ranked set sampling in the presence of missing data. This article introduces novel imputation methods designed to estimate population means in the context of missing data under RSS. These innovative estimators are developed by integrating ratio, exponential, and logarithmic estimators judiciously. Expressions for the bias and mean squared error of the proposed estimators are derived up to the first-order approximation. Through simulation studies and an application to stunting and its determinants among children in Uttar Pradesh, India’s most populous state, the effectiveness of the suggested estimators in handling missing data is demonstrated. Numerical examples involving stunting in Uttar Pradesh, as well as simulated data generated using R software, confirm the superior performance of the proposed estimators over existing methods, as evidenced by comparisons of percentage relative efficiency and mean squared error. The results are promising, indicating improvement over all existing imputation methods. Additionally, pertinent recommendations are provided for survey professionals regarding future research.

Risk of stress cardiomyopathy associated with selective serotonin reuptake inhibitors and serotonin and norepinephrine reuptake inhibitors: a real-world pharmacovigilance analysis

Selective serotonin reuptake inhibitors (SSRIs) and serotonin and norepinephrine reuptake inhibitors (SNRIs) are reported to cause stress cardiomyopathy (SC). This study evaluated the association between SSRI/SNRI use and the occurrence of cardiomyopathy in the publicly available U.S. Food and Drug Administration Adverse Event Reporting System (FAERS) database. Disproportionate analysis and likelihood ratio tests were used to identify risk associated with SSRIs or SNRIs and the incidence of SC, using data from between from 2012 to 2022 acquired from the FAERS database. The study identified 132 individual case safety reports (ICSRs) of SC associated with SSRIs or SNRIs. Venlafaxine (48%) and fluoxetine (27%) were the most common antidepressants of the ICSRs. Approximately 80% of SC cases were reported in females, with individuals aged 45–65 years identified as a high-risk population. Both venlafaxine (ratio-scale information component [RSIC] 2.54, 95% CI 2.06–3.04) and fluoxetine (RSIC 3.20, 95% CI 2.31–4.47) were associated with SC, with likelihood ratio estimates of 3.55 (p = 0.02) for venlafaxine and 4.82 (p = 0.008) for fluoxetine. The median time to cardiomyopathy onset was 20 days, with hospitalization reported in 48.33% of patients. Venlafaxine and fluoxetine were associated with SC risk, particularly in middle-aged women. Caution should be exercised when using SSRIs or SNRIs combined with other serotonergic medications.

Estimator Comparison for the Prediction of Election Results

Cluster randomized experiments and estimator comparisons are well-documented topics. In this paper, using the datasets of the popular vote in the presidential elections of the United States of America (2012, 2016, 2020), we evaluate the properties (SE, MSE) of three cluster sampling estimators: Ratio estimator, Horvitz–Thompson estimator and the linear regression estimator. While both the Ratio and Horvitz–Thompson estimators are widely used in cluster analysis, we propose a linear regression estimator defined for unequal cluster sizes, which, in many scenarios, performs better than the other two. The main objective of this paper is twofold. Firstly, to indicate which estimator is most suited for predicting the outcome of the popular vote in the United States of America. We do so by applying the single-stage cluster sampling technique to our data. In the first partition, we use the 50 states plus the District of Columbia as primary sampling units, whereas in the second one, we use 3112 counties instead. Secondly, based on the results of the aforementioned procedure, we estimate the number of clusters in a sample for a set standard error while also considering the diminishing returns from increasing the number of clusters in the sample. The linear regression estimator is best in the majority of the examined cases. This type of comparison can also be used for the estimation of any other country’s elections if prior voting results are available.

Modeling highly crosslinked polyethylene vs. non–highly crosslinked polyethylene glenoid revision rates for anatomic shoulder arthroplasty in osteoarthritis including differing polyethylene glenoid fixation designs

BackgroundWe compared anatomic total shoulder arthroplasty (aTSA) for osteoarthritis to both highly crosslinked polyethylene (XLPE) and non–highly crosslinked polyethylene (non-XLPE) to determine the rate of revision for multiple patient and implant characteristics, modeling the effect of variation in glenoid fixation design (glenoid component type). MethodsData from a large national arthroplasty registry were analyzed for the period April 16, 2004, to December 31, 2022. The study population included all primary aTSA (stemmed and stemless shoulder arthroplasty) procedures with a primary diagnosis of osteoarthritis and performed using prostheses in current use. These procedures were grouped into 2 cohorts: all polyethylene-bearing glenoid components with either XLPE or non-XLPE. The cumulative percent revision was determined using Kaplan–Meier estimates of survivorship and hazard ratios from Cox proportional hazard models adjusted for age, sex, humeral head size, humeral fixation, type of primary (total stemmed or stemless anatomic), glenoid component type (modular and nonmodular metal-backed glenoid, cemented polyethylene glenoid, and polyethylene glenoid with modified central peg), and surgeon volume (after 2008). Possible interactions were examined. A subanalysis from January 1, 2017, captured the additional patient demographics of American Society of Anesthesiologists score, body mass index, and glenoid morphology. ResultsOf 11,003 aTSA procedures, the cumulative percent revision at 14 years for all XLPE glenoids (n = 3865) was 5.8% (95% confidence interval [CI] 3.9, 8.7), and 18.7% (95% CI 16.6, 21.0) for non-XLPE (n = 7138). XLPE had a lower rate of revision from 2 years (non-XLPE vs. XLPE 2 years + hazard ratio = 1.66, (95% CI 1.09, 2.53), P = .018) adjusting for age, sex, humeral head size, type of primary, humeral stem fixation, and glenoid component type. Overall, glenoid component type and polyethylene type were strongly associated (P < .001 and P = .021, respectively) with all-cause aTSA revision rates. The difference between non-XLPE and XLPE is observed across all polyethylene glenoid types. When considering procedures performed between 2017 and 2022, XLPE vs. non-XLPE rates of revision were not significantly different with extended adjustment at subanalysis, but loosening did not predominate for non-XLPE until year 6 of follow-up. ConclusionBoth the glenoid design and the type of polyethylene predict the revision rate for aTSA. However, the relative rates of revision between glenoid designs did not differ with polyethylene type. While the polyethylene type was not associated with aTSA revision rates in a more contemporary analysis, the incidence of loosening in non-XLPE prostheses combinations was higher from 6 years may explain this.

Exploring single-shot propagation and speckle based phase recovery techniques for object thickness estimation by using a polychromatic X-ray laboratory source.

Propagation and speckle-based techniques allow reconstruction of the phase of an X-ray beam with a simple experimental setup. Furthermore, their implementation is feasible using low-coherence laboratory X-ray sources. We investigate different approaches to include X-ray polychromaticity for sample thickness recovery using such techniques. Single-shot Paganin (PT) and Arhatari (AT) propagation-based and speckle-based (ST) techniques are considered. The radiation beam polychromaticity is addressed using three different averaging approaches. The emission-detection process is considered for modulating the X-ray beam spectrum. Reconstructed thickness of three nylon-6 fibers with diameters in the millimeter-range, placed at various object-detector distances are analyzed. In addition, the thickness of an in-house made breast phantom is recovered by using multi-material Paganin's technique (MPT) and compared with micro-CT data. The best quantitative result is obtained for the PT and ST combined with sample thickness averaging (TA) approach that involves individual thickness recovery for each X-ray spectral component and the smallest considered object-detector distance. The error in the recovered fiber diameters for both techniques is , despite the higher noise level in ST images. All cases provide estimates of fiber diameter ratios with an error of 3% with respect to the nominal diameter ratios. The breast phantom thickness difference between MPT-TA and micro-CT is about 10%. We demonstrate the single-shot PT-TA and ST-TA techniques feasibility for thickness recovery of millimeter-sized samples using polychromatic microfocus X-ray sources. The application of MPT-TA for thicker and multi-material samples is promising.

Enhanced direct and synthetic estimators for domain mean with simulation and applications

This article considers the issue of domain mean estimation utilizing bivariate auxiliary information based enhanced direct and synthetic logarithmic type estimators under simple random sampling (SRS). The expressions of mean square error (MSE) of the proposed estimators are provided to the 1st order approximation. The efficiency criteria are derived to exhibit the dominance of the proposed estimators. To exemplify the theoretical results, a simulation study is conducted on a hypothetically drawn trivariate normal population from R programming language. Some applications of the suggested methods are also provided by analyzing the actual data from the municipalities of Sweden and acreage of paddy crop in the Mohanlal Ganj tehsil of the Indian state of Uttar Pradesh. The findings of the simulation and real data application exhibit that the proposed direct and synthetic logarithmic estimators dominate the conventional direct and synthetic mean, ratio, and logarithmic estimators in terms of least MSE and highest percent relative efficiency.

Improved memory-type ratio estimator for population mean in stratified random sampling under linear and non-linear cost functions

This paper offers an improved memory-type ratio estimator in stratified random sampling under linear and non-linear cost functions. The issue is given as all integer non-linear programming problems (AINLPPs). The sampling properties mainly the bias and the mean squared error of the introduced estimator are derived up to the first order of approximation. The optimum value of the characterizing scalar is obtained by the Lagrange method of maxima–minima. The least value of the MSE of the suggested estimator is also obtained for this optimum value of the charactering constant. The suggested estimator is compared both theoretically and empirically with the competing estimators. Under this setup, the optimum allocation with mean square error of the suggested estimator is attained, and the estimator is compared to other comparable estimators. The AINLPP is solved using the genetic programming approach, which is applied to both actual and simulated data sets from a bivariate normal distribution.

A Class of Ratio Estimators for Estimating Population Mean Using a Transformed Auxiliary Variable Under Stratified Random Sampling

A Class of Ratio Estimators for Estimating Population Mean Using a Transformed Auxiliary Variable Under Stratified Random Sampling

A hybrid approach to event reconstruction for atmospheric Cherenkov Telescopes combining machine learning and likelihood fitting

The imaging atmospheric Cherenkov technique provides potentially the highest angular resolution achievable in astronomy at energies above the X-ray waveband. High-resolution measurements provide the key to progress on many of the major questions in high energy astrophysics, including the sites and mechanisms of particle acceleration to PeV energies. The huge potential of the next-generation CTA observatory in this regard can be realised with the help of improved algorithms for the reconstruction of the air-shower direction and energy.Hybrid methods combining maximum-likelihood-fitting techniques with neural networks represent a particularly promising approach and have recently been successfully applied for the reconstruction of astrophysical neutrinos. Here, we present the FreePACT algorithm, a hybrid reconstruction method for IACTs. In this, making use of the neural ratio estimation technique from the field of likelihood-free inference, the analytical likelihood used in traditional image likelihood fitting is replaced by a neural network that approximates the charge probability density function for each pixel in the camera.The performance of this improved algorithm is demonstrated using simulations of the planned CTA southern array. For this setupFreePACT provides significant performance improvements over analytical likelihood techniques, with improvements in angular and energy resolution of 25% or more over a wide energy range and an angular resolution as low as 40′′ at energies above 50TeV for observations at 20° zenith angle. It also yields more accurate estimations of the uncertainties on the reconstructed parameters and significantly speeds up the reconstruction compared to analytical likelihood techniques while showing the same stability with respect to changes in the observation conditions. Therefore, the FreePACT method is a promising upgrade over the current state-of-the-art likelihood event reconstruction techniques.

Contributing factors for reduction in maternal mortality ratio in India

Maternal mortality ratio (MMR) estimates have been studied over time for understanding its variation across the country. However, it is never sufficient without accounting for presence of variability across in terms of space, time, maternal and system level factors. The study endeavours to estimate and quantify the effect of exposures encompassing all maternal health indicators and system level indicators along with space–time effects influencing MMR in India. Using the most recent level of possible -factors of MMR, maternal health indicators from the National Family Health Survey (NFHS: 2019–21) and system level indicators from government reports a heatmap compared the relative performance of all 19 SRS states. Facet plots with a regression line was utilised for studying patterns of MMR for different states in one frame. Using Bayesian Spatio-temporal random effects, evidence for different MMR patterns and quantification of spatial risks among individual states was produced using estimates of MMR from SRS reports (2014–2020). India has witnessed a decline in MMR, and for the majority of the states, this drop is linear. Few states exhibit cyclical trend such as increasing trends for Haryana and West Bengal which was evident from the two analytical models i.e., facet plots and Bayesian spatio- temporal model. Period of major transition in MMR levels which was common to all states is identified as 2009–2013. Bihar and Assam have estimated posterior probabilities for spatial risk that are relatively greater than other SRS states and are classified as hot spots. More than the individual level factors, health system factors account for a greater reduction in MMR. For more robust findings district level reliable estimates are required. As evident from our study the two most strong health system influencers for reducing MMR in India are Institutional delivery and Skilled birth attendance.

Population-Based Risk of Psychiatric Disorders Associated With Recurrent Copy Number Variants

Recurrent copy number variants (rCNVs) have been associated with increased risk of psychiatric disorders in case-control studies, but their population-level impact is unknown. To provide unbiased population-based estimates of prevalence and risk associated with psychiatric disorders for rCNVs and to compare risks across outcomes, rCNV dosage type (deletions or duplications), and locus features. This genetic association study is an analysis of data from the Lundbeck Foundation Initiative for Integrative Psychiatric Research (iPSYCH) case-cohort sample of individuals born in Denmark in 1981-2008 and followed up until 2015, including (1) all individuals (n = 92 531) with a hospital discharge diagnosis of attention-deficit/hyperactivity disorder (ADHD), autism spectrum disorder (ASD), bipolar disorder, major depressive disorder (MDD), or schizophrenia spectrum disorder (SSD) and (2) a subcohort (n = 50 625) randomly drawn from the source population. Data were analyzed from January 2021 to August 2023. Carrier status of deletions and duplications at 27 autosomal rCNV loci was determined from neonatal blood samples genotyped on single-nucleotide variant microarrays. Population-based rCNV prevalence was estimated with a survey model using finite population correction to account for oversampling of cases. Hazard ratio (HR) estimates and 95% CIs for psychiatric disorders were derived using weighted Cox proportional hazard models. Risks were compared across outcomes, dosage type, and locus features using generalized estimating equation models. A total of 3547 rCNVs were identified in 64 735 individuals assigned male at birth (53.8%) and 55 512 individuals assigned female at birth (46.2%) whose age at the end of follow-up ranged from 7.0 to 34.7 years (mean, 21.8 years). Most observed increases in rCNV-associated risk for ADHD, ASD, or SSD were moderate, and risk estimates were highly correlated across these disorders. Notable exceptions included high ASD-associated risk observed for Prader-Willi/Angelman syndrome duplications (HR, 20.8; 95% CI, 7.9-55). No rCNV was associated with increased MDD risk. Also, rCNV-associated risk was positively correlated with locus size and gene constraint but not with dosage type. Comparison with published case-control and community-based studies revealed a higher prevalence of deletions and lower associated increase in risk for several rCNVs in iPSYCH2015. This study found that several rCNVs were more prevalent and conferred less risk of psychiatric disorders than estimated previously. Most case-control studies overestimate rCNV-associated risk of psychiatric disorders, likely because of selection bias. In an era where genetics is increasingly being clinically applied, these results highlight the importance of population-based risk estimates for genetics-based predictions.

Bayesian Estimation Improves Prediction of Outcomes after Epilepsy Surgery.

Low power is a problem in many fields, as underpowered studies that find a statistically significant result will exaggerate the magnitude of the observed effect size. We quantified the statistical power and magnitude error of studies of epilepsy surgery outcomes. The median power across all studies was 14%. Studies with a median sample size or less (n<=56) and a statistically significant result exaggerated the true effect size by a factor of 5.4 (median odds ratio 9.3 vs. median true odds ratio 1.7), while the Bayesian estimate of the odds ratio only exaggerated the true effect size by a factor of 1.6 (2.7 vs. 1.7). We conclude that Bayesian estimation of odds ratio attenuates the exaggeration of significant effect sizes in underpowered studies. This approach could help improve patient counseling about the chance of seizure freedom after epilepsy surgery.

The Association Between the Location of Subarachnoid Hemorrhage and the Occurrence of Takotsubo Cardiomyopathy: A Systematic Review and Meta-analysis.

Takotsubo cardiomyopathy (TCM) is a syndrome characterized by transient regional cardiac dysfunction of the left ventricle. The goal of this review is to better understand the relationship between the anatomic locations of subarachnoid hemorrhages (SAHs) and the development of TCM as identified through a review of cohort studies. From inception to December 2023, we systematically explored major electronic medical information sources to identify cases of TCM that developed after SAHs. The six selected studies included in the meta-analysis suggest a modest but statistically significant increase in the odds of the events in the posterior circulation group compared to the anterior circulation group, with a combined odds ratio (OR) estimate of around 1.45-1.46. The fixed effect model gives an overall OR of 1.45 with a 95% confidence interval (CI) of 1.01 to 2.10, z = 2.01, p = 0.0442, while the random effects model yields a slightly higher OR of 1.46 with the same 95% CI, z = 2.03, p = 0.0425. There is a tendency for SAH occurrence in the posterior cerebral circulation to cause SAH-related TCM more frequently than in the anterior cerebral circulation.

The Complications of Osseous Reconstruction in the Head and Neck: A Systematic Review and Meta-analysis.

To compare the postoperative complications of the fibular free flap (FFF), scapula free flap (SFF), and osteocutaneous radial forearm free flap (OCRFFF) following osseous reconstruction in the head and neck. PUBMED, EMBASE, Cochrane. A literature search and systematic review were performed using the Preferred Reporting Items for Systematic Reviews and Meta-analysis guidelines. A meta-analysis of proportions was conducted using a random effects model to compare operative time and postoperative complications. The systematic review identified 26 studies comparing at least 1 variable of interest. The odds ratio estimates favored reduced rates of flap failure with the OCRFFF when compared to FFF (0.7, confidence interval [CI]: 0.29-1.11, P < .001), while FFF and SFF were similar. The mean difference estimates for operative time significantly favored FFF over SFF (-51.04 minutes, CI: -92.73 to -9.35, P = .016) and OCRFFF over FFF (66.77 minutes, CI: 52.74-80.8, P < .001). The FFF was more prone to hardware exposure, longer hospital stays, and donor site complications. Recipient wound complications and fistula rates were similar for all flap types. Depending on the clinical context, the OCRFFF, FFF, and SFF are all robust options for reconstruction in the head and neck. The OCRFFF is associated with a reduced rate of flap failure and shorter operative times. The SFF requires longer operative times, although significant variation was observed between institutions. The FFF has broad reconstructive indications but is associated with more perioperative and long-term complications.