OBJECTIVE: Does the Mind/Body Program for Infertility (MBPI) perform better, due to certain distinctive elements, than a partly matched support group in improving the wellbeing and medically assisted reproduction (MAR) outcomes of women with elevated distress levels in a clinical setting?
CONCLUSIONS: While robust enhancements occurred in the wellbeing overall, the cognitive behavioural and formalized stress management elements of the MBPI allowed a significantly stronger improvement in trait anxiety, but not in other mental health and MAR outcomes, compared with a support group.
BACKGROUND: Mind-body psychological programmes adjacent to MAR have been found to improve women\'s mental states and possibly increase chances of pregnancy. However, not enough is known about the programme\'s effectiveness among patients with elevated distress levels in routine clinical settings, nor is it clear which of its particular ingredients are specifically effective.
METHODS: A pre-post design, single-centre, randomized controlled trial was performed between December 2019 and October 2022 (start and end of recruitment, respectively). The sample size (n = 168) was calculated to detect superiority of the MBPI in improving fertility-related quality of life. Randomization was computer-based, with random numbers concealing identities of patients until after allocation.
METHODS: The trial was conducted at a large university teaching hospital. A total of 168 patients were randomly assigned to the mind-body (MBPI) group (n = 84) and the fertility support (FS) control group (n = 84). Patients received a 10-week, 135-min/week group intervention, with the FS group following the same format as the MBPI group, but with a less restricted and systematic content, and without the presumed effective factors. The number of patients analysed was n = 74 (MBPI) and n = 68 (FS) for post-intervention psychological outcomes, and n = 54 (MBPI) and n = 56 (FS) for pregnancy outcomes at a 30-month follow-up.
RESULTS: Significant improvements occurred in both groups in all psychological domains (adjusted P < 0.001), except for treatment-related quality of life. Linear mixed-model regression analysis did not reveal significantly greater pre-post improvements in the MBPI group than in the FS group in fertility-related quality of life (difference in differences (DD) = 4.11 [0.42, 7.80], d = 0.32, adjusted P = 0.124), treatment-related quality of life (DD = -3.08 [-7.72, 1.55], d = -0.20, adjusted P = 0.582), infertility-specific stress (DD = -2.54 [-4.68, 0.41], d = -0.36, adjusted P = 0.105), depression (DD = -1.16 [3.61, 1.29], d = -0.13, adjusted P = 0.708), and general stress (DD = -0.62 [-1.91, 0.68], d = -0.13, adjusted P = 0.708), but it did show a significantly larger improvement in trait anxiety (DD = -3.60 [-6.16, -1.04], d = -0.32, adjusted P = 0.042). Logistic regression showed no group effect on MAR pregnancies, spontaneous pregnancies, or live births.
CONCLUSIONS: The follow-up only covered MAR-related medical outcomes and no psychological variables, and their rates were not equal in the two groups. Biological factors other than age, aetiology, and duration of infertility may have confounded the study results. Loss to follow-up was between 5% and 10%, which may have led to some bias.
CONCLUSIONS: The psychologically and medically heterogeneous sample, the normal clinical setting and the low attrition rate all raise the external validity and generalizability of our study. The MBPI works not only in controlled conditions, but also in routine MAR practice, where it can be introduced as a cost-effective, low-intensity psychological intervention, within the framework of stepped care. More studies are needed to further identify its active ingredients.
BACKGROUND: The authors received no financial support for the research, authorship, and/or publication of this article. The authors have no conflict of interest to disclose.
BACKGROUND: ClinicalTrials.gov NCT04151485.
UNASSIGNED: 5 November 2019.
UNASSIGNED: 15 December 2019.

BACKGROUND: The COVID-19 pandemic is affecting the mental and emotional well-being of patients, family members, and health care workers. Patients in the isolation ward may have psychological problems due to long-term hospitalization, the development of the epidemic, and the inability to see their families. A medical assistive robot (MAR), acting as an intermediary of communication, can be deployed to address these mental pressures.
OBJECTIVE: CareDo, a MAR with telepresence and teleoperation functions, was developed in this work for remote health care. The aim of this study was to investigate its practical performance in the isolation ward during the pandemic.
METHODS: Two systems were integrated into the CareDo robot. For the telepresence system, a web real-time communications solution is used for the multiuser chat system and a convolutional neural network is used for expression recognition. For the teleoperation system, an incremental motion mapping method is used for operating the robot remotely. A clinical trial of this system was conducted at First Affiliated Hospital, Zhejiang University.
RESULTS: During the clinical trials, tasks such as video chatting, emotion detection, and medical supplies delivery were performed via the CareDo robot. Seven voice commands were set for performing system wakeup, video chatting, and system exiting. Durations from 1 to 3 seconds of common commands were set to improve voice command detection. The facial expression was recorded 152 times for a patient in 1 day for the psychological intervention. The recognition accuracy reached 95% and 92.8% for happy and neutral expressions, respectively.
CONCLUSIONS: Patients and health care workers can use this MAR in the isolation ward for telehealth care during the COVID-19 pandemic. This can be a useful approach to break the chains of virus transmission and can also be an effective way to conduct remote psychological intervention.

Early accurate radiographic assessment of peri-implant bone condition is highly important to avoid excessive loss of supporting bone and implant failure. Cone beam computed tomography (CBCT) is the radiographic technique of choice if peri-implant dehiscence and fenestration defects are suspected. The higher radiation dose and the presence of beam hardening artifacts are the main drawbacks of CBCT imaging techniques. This study aims to evaluate the influence of low-dose cone beam computed tomography (LD-CBCT) and metal artifact reduction (MAR) tool on the assessment of peri-implant dehiscence and fenestration.
Thirty titanium implants were inserted into bovine rib blocks. Twenty had standardized bone defects (10 with dehiscence and 10 with fenestration), while the remaining 10 were used as control group with no defects. Radiographic examinations held with high-definition CBCT (HD-CBCT) and LD-CBCT with and without application of MAR tool. Images were assessed by four examiners for the presence or absence of peri-implant defects. The area under the area under the receiver operating characteristic (ROC) curve (AUC), sensitivity, specificity, and accuracy were calculated for all radiographic protocols.
In the absence of MAR tool, there was no difference in AUC and diagnostic values between LD-CBCT and HD-CBCT for detection of both defects. When the MAR tool was applied, the AUC values, sensitivity, and accuracy were higher in HD-CBCT than in LD-CBCT for the detection of both defects, especially for the dehiscence, while specificity remained the same.
LD-CBCT can be used in the evaluation of peri-implant dehiscence and fenestration without any decrease in diagnostic accuracy. The application of MAR tool decrease the diagnostic ability of both defects, especially for the detection of dehiscence defects.

OBJECTIVE: The aim of this study is to assess the effects of metal artifact reduction (MAR) and adaptive image noise enhancer (AINO) in CBCT imaging on the detection accuracy of artificially created fenestration defects in proximity to titanium and zirconium implants in sheep jaw.
METHODS: Six zirconium and 10 titanium implants were planted on mandibular jaws of three sheep, and artificial defects were created. All images were obtained with a standard voxel size (0.150 mm3) and with 4 scan modes: (1) without MAR/without AINO; (2) with MAR/without AINO; (3) without MAR/with AINO; and (4) with MAR/with AINO during CBCT scanning. A total of 60 CBCT scans were produced.
RESULTS: For all types of implants, intra- and inter-observer kappa values were the highest for MAR filter. The scan mode of with MAR filter was found to have the highest area under the curve (AUC), whereas the scan mode of without both MAR and AINO filters was found to have the lowest AUC values with statistical significance (p ≤ 0.05). Titanium implants were found to have higher AUC values than zirconium (p ≤ 0.05).
CONCLUSIONS: Both MAR module and AINO filters enhance the accuracy of the detection of peri-implant fenestrations; however, the use of MAR filter solely can be recommended for detection of peri-implant fenestrations.

Missing data are common in end-of-life care studies, but there is still relatively little exploration of which is the best method to deal with them, and, in particular, if the missing at random (MAR) assumption is valid or missing not at random (MNAR) mechanisms should be assumed. In this paper we investigated this issue through a sensitivity analysis within the ACTION study, a multicenter cluster randomized controlled trial testing advance care planning in patients with advanced lung or colorectal cancer.
Multiple imputation procedures under MAR and MNAR assumptions were implemented. Possible violation of the MAR assumption was addressed with reference to variables measuring quality of life and symptoms. The MNAR model assumed that patients with worse health were more likely to have missing questionnaires, making a distinction between single missing items, which were assumed to satisfy the MAR assumption, and missing values due to completely missing questionnaire for which a MNAR mechanism was hypothesized. We explored the sensitivity to possible departures from MAR on gender differences between key indicators and on simple correlations.
Up to 39% of follow-up data were missing. Results under MAR reflected that missingness was related to poorer health status. Correlations between variables, although very small, changed according to the imputation method, as well as the differences in scores by gender, indicating a certain sensitivity of the results to the violation of the MAR assumption.
The findings confirmed the importance of undertaking this kind of analysis in end-of-life care studies.

BACKGROUND: LC-MS technology makes it possible to measure the relative abundance of numerous molecular features of a sample in single analysis. However, especially non-targeted metabolite profiling approaches generate vast arrays of data that are prone to aberrations such as missing values. No matter the reason for the missing values in the data, coherent and complete data matrix is always a pre-requisite for accurate and reliable statistical analysis. Therefore, there is a need for proper imputation strategies that account for the missingness and reduce the bias in the statistical analysis.
RESULTS: Here we present our results after evaluating nine imputation methods in four different percentages of missing values of different origin. The performance of each imputation method was analyzed by Normalized Root Mean Squared Error (NRMSE). We demonstrated that random forest (RF) had the lowest NRMSE in the estimation of missing values for Missing at Random (MAR) and Missing Completely at Random (MCAR). In case of absent values due to Missing Not at Random (MNAR), the left truncated data was best imputed with minimum value imputation. We also tested the different imputation methods for datasets containing missing data of various origin, and RF was the most accurate method in all cases. The results were obtained by repeating the evaluation process 100 times with the use of metabolomics datasets where the missing values were introduced to represent absent data of different origin.
CONCLUSIONS: Type and rate of missingness affects the performance and suitability of imputation methods. RF-based imputation method performs best in most of the tested scenarios, including combinations of different types and rates of missingness. Therefore, we recommend using random forest-based imputation for imputing missing metabolomics data, and especially in situations where the types of missingness are not known in advance.

Medically assisted reproduction (MAR) technologies have advanced rapidly, but in contrast to the specificity of modern approaches, they provide limited effectiveness in the management of the infertile couple. The purpose of this study was to assess the possible relationship between age at menarche and MAR outcomes of clinical pregnancy, live birth and the adverse incident of miscarriage, and to determine the offspring sex ratio according to age at menarche. In a cohort of 254 infertile couples who underwent 426 IVF/ICSI cycles, statistical analysis was performed by applying Student\'s t-test, chi-square test, and logistic regression models, adequately in the respective parameters and outcomes. The results indicated a strong association of age at menarche with the outcomes of clinical pregnancy (p = .0007) and live birth (p < .0001), especially by applying a threshold of 12 years in the first occurrence of menstruation (p = .0019 for clinical pregnancy, p < .0001 for live birth), also demonstrating a negative effect for earlier menarche that acts in parallel with the increasing age of the woman. Calculation of sex ratio demonstrated a tendency towards female offspring close to the age at menarche of 12 years. Age at menarche could serve as a surrogate parameter for reproductive potential towards personalized management of infertility.

OBJECTIVE: To evaluate the effect of luteal phase support (LPS) in intrauterine insemination (IUI) cycles stimulated with gonadotropins.
METHODS: Randomized multicenter trial.
METHODS: Academic tertiary care centers and affiliated secondary care centers.
METHODS: Three hundred and ninety-three normo-ovulatory patients, <43 years, with body mass index ≤30 kg/m2, in their first IUI cycle, with at least one patent tube, a normal uterine cavity, and a male partner with total motile sperm count ≥5 million after capacitation.
METHODS: Gonadotropin stimulation, IUI, randomization to LPS using vaginal progesterone gel (n = 202) or no LPS (n = 191).
METHODS: Clinical pregnancy rate, live-birth rate, miscarriage rate, and duration of the luteal phase.
RESULTS: The primary outcome, the clinical pregnancy rate, was not statistically different between the treatment group (16.8%) and the control group (11%) (relative risk [RR] 1.54; 95% confidence interval [CI], 0.89-2.67). Similarly, the secondary outcome, the live-birth rate, was 14.9% in the treatment group and 9.4% in the control group (RR 1.60; 95% CI, 0.89-2.87). The mean duration of the luteal phase was about 2 days longer in the treatment group (16.6 ± 2.2 days) compared with the control group (14.6 ± 2.5 days) (mean difference 2.07; 95% CI, 1.58-2.56).
CONCLUSIONS: Although a trend toward a higher clinical pregnancy rate as well as live-birth rate was observed in the treatment group, the difference with the control group was not statistically significant.
BACKGROUND: NCT01826747.

The problem of missing data occurs frequently in veterinary epidemiological studies. Most studies use a complete case (CC) analysis which excludes all observations for which any relevant variable have missing values. Alternative approaches (most notably multiple imputation (MI)) which avoid the exclusion of observations with missing values are now widely available but have been used very little in veterinary epidemiology. This paper uses a case study based on research into dairy producers\' attitudes toward mastitis control procedures, combined with two simulation studies to evaluate the use of MI and compare results with a CC analysis. MI analysis of the original data produced results which had relatively minor differences from the CC analysis. However, most of the missing data in the original data set were in the dependent variable and a subsequent simulation study based on the observed missing data pattern and 1000 simulations showed that an MI analysis would not be expected to offer any advantages over a CC analysis in this situation. This was true regardless of the missing data mechanism (MCAR - missing completely at random, MAR - missing at random, or NMAR - not missing at random) underlying the missing values. Surprisingly, recent textbooks dealing with MI make little reference to this limitation of MI for dealing with missing values in the dependent variable. An additional simulation study (1000 runs for each of the three missing data mechanisms) compared MI and CC analyses for data in which varying levels (n=7) of missing data were created in predictor variables. This study showed that MI analyses generally produced results that were less biased on average, were more precise (smaller SEs), were more consistent (less variability between simulation runs) and consequently were more likely to produce estimates that were close to the \"truth\" (results obtained from a data set with no missing values). While the benefit of MI varied with the mechanism used to generate the missing data, MI always performed as well as, or better than, CC analysis.

OBJECTIVE: Metallic dental implants cause severe streaking artifacts in computed tomography (CT) data, which affect the accuracy of dose calculations in radiation therapy. The aim of this study was to investigate the benefit of the metal artifact reduction algorithm iterative metal artifact reduction (iMAR) in terms of correct representation of Hounsfield units (HU) and dose calculation accuracy.
METHODS: Heterogeneous phantoms consisting of different types of tissue equivalent material surrounding metallic dental implants were designed. Artifact-containing CT data of the phantoms were corrected using iMAR. Corrected and uncorrected CT data were compared to synthetic CT data to evaluate accuracy of HU reproduction. Intensity-modulated radiation therapy (IMRT) and volumetric modulated arc therapy (VMAT) plans were calculated in Oncentra v4.3 on corrected and uncorrected CT data and compared to Gafchromic™ EBT3 films to assess accuracy of dose calculation.
RESULTS: The use of iMAR increased the accuracy of HU reproduction. The average deviation of HU decreased from 1006 HU to 408 HU in areas including metal and from 283 HU to 33 HU in tissue areas excluding metal. Dose calculation accuracy could be significantly improved for all phantoms and plans: The mean passing rate for gamma evaluation with 3 % dose tolerance and 3 mm distance to agreement increased from 90.6 % to 96.2 % if artifacts were corrected by iMAR.
CONCLUSIONS: The application of iMAR allows metal artifacts to be removed to a great extent which leads to a significant increase in dose calculation accuracy.