title stringlengths 1 1.19k | keywords stringlengths 0 668 | concept stringlengths 0 909 | paragraph stringlengths 0 61.8k | PMID stringlengths 10 11 |
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Primary outcome | In the adjusted model (Table Adjusted mean differences [95% confidence interval] of study outcomes based on mixed effects models | PMC10054219 | ||
Secondary staff outcomes | SECONDARY | There was no statistically significant difference in any of the secondary staff outcomes, based on adjusted mean scores at follow-up between the intervention and control group (Table Change in scores measuring how often care aides suggested ways to improve performance to their colleagues by level of enactment based on ... | PMC10054219 | |
Resident care outcomes | No intervention effects were found for those teams working on responsive behaviors. However, the adjusted level of resident dependency significantly decreased for residents whose teams addressed mobility ( | PMC10054219 | ||
Discussion | SCOPE was a multicomponent intervention designed to facilitate the use of best practices in care for older LTC home residents. SCOPE incorporated elements of design to support sustainability and address the need for programs of research on implementation and improvement in healthcare [ | PMC10054219 | ||
Primary outcome | SCOPE was initially planned as a randomized clinical trial, attempting to take the complex nature of the system in which it was implemented into account. Using quantitative measures, SCOPE’s primary aim, improvement in the conceptual use of best practices, was not demonstrated. The intention was that teams would implem... | PMC10054219 | ||
Secondary outcomes—staff | SECONDARY | There were no statistically significant group differences in staff-related secondary outcomes. These findings should inform sample size calculations of future studies of this nature for example, if based on the size of effects we report using similar outcomes measures.A post hoc analysis revealed a statistically signif... | PMC10054219 | |
Secondary outcomes—residents | pain | Examination of two of the three resident care indicators from the RAI-MDS showed no difference in change between groups over the period of observation. The finding in pain assessment is however, encouraging, suggesting that teams were successful in improving the quality of care for residents in pain, but this is a sing... | PMC10054219 | |
Limitations | SECONDARY, RECRUITMENT | We note above limitations related to the primary and secondary outcomes. There are additional important limitations. While initially intended as a randomized controlled trial with propensity matching to TREC units not participating in SCOPE, two problems were encountered that were eventually insurmountable, resulting i... | PMC10054219 | |
Conclusion | In conventional terms, SCOPE was a negative study further contributing to the file drawer problem [ | PMC10054219 | ||
Acknowledgements | McLeod | We would like to thank the LTC homes and their care teams who participated in this study. We would also like to thank Don McLeod for facilitating the Learning Congresses and contributing to the development of the SCOPE materials for participants; the Quality Advisors (Carolyn Brandly, Fiona MacKenzie, Barb Stolee) for ... | PMC10054219 | |
Authors’ contributions | MH | AW led the study and led the writing of the protocol. AW, MH, LG, WB, MD, JK-S, YS, PN, and CE were all involved in the conduct of the study. AW, PN, LG, and MH developed the data analysis plan, conducted the analysis, and interpreted the results. MH drafted all tables. AW wrote the first draft of the manuscript and le... | PMC10054219 | |
Funding | This study was funded by a Canadian Institutes of Health Research (CIHR) Operating Grant CIHR PS 148582 Wagg and funds from the Muhlenfeld Family Trust held by Dr. Wagg. | PMC10054219 | ||
Availability of data and materials | The data used for this article are housed in the secure and confidential Health Research Data Repository (HRDR) in the Faculty of Nursing at the University of Alberta ( | PMC10054219 | ||
Declarations | PMC10054219 | |||
Ethics approval consent to participate | This study was approved by the Research Ethics Boards of the University of Alberta (Pro00012517) and University of British Columbia (H14-03286). Operational approval was obtained from all included facilities as required. SCOPE sponsors and team members were asked for oral informed consent before participating in any pr... | PMC10054219 | ||
Consent for publication | Not applicable. | PMC10054219 | ||
Competing interests | The authors declare that they have no competing interests. | PMC10054219 | ||
References | PMC10054219 | |||
INTRODUCTION | REGRESSION | Many trials use stratified randomisation, where participants are randomised within strata defined by one or more baseline covariates. While it is important to adjust for stratification variables in the analysis, the appropriate method of adjustment is unclear when stratification variables are affected by misclassificat... | PMC7614797 | |
SIMULATION METHODS | A simulation study was conducted to investigate the impact of stratification errors on the analysis of trials that use stratified randomisation. Continuous outcomes for the For each scenario, 10 000 simulated datasets were generated and analysed using Stata 16.1 (StataCorp, College Station, Texas, USA). Simulation resu... | PMC7614797 | ||
Effect of stratification errors on the correlation between treatment groups | The previous finding that stratification induces correlation between the sample mean outcomes in the treatment groups | PMC7614797 | ||
Estimating treatment effects when all stratification errors are discovered | To compare the performance of different methods of adjusting for stratification variables when all stratification errors are discovered, outcomes were generated from model (1) with Using no additional covariates (unadjusted analysis),a main effect for a main effect for While adjusting for both Letting bias in the param... | PMC7614797 | ||
Estimating treatment effects when only some stratification errors are discovered | To compare the performance of different methods of adjusting for stratification variables when only some stratification errors are discovered, the simulation study described in Section | PMC7614797 | ||
Estimating treatment by covariate interaction effects | SECONDARY | To explore the impact of stratification errors on treatment‐by‐covariate interaction tests, outcomes were generated from model (1) with The primary estimand of interest was A secondary estimand of interest was the marginal treatment effect | PMC7614797 | |
Sensitivity of results to sample size and covariate prevalence | To explore the sensitivity of our simulation results to the chosen sample size and covariate prevalence, we repeated a subset of simulations (see Table | PMC7614797 | ||
SIMULATION RESULTS | PMC7614797 | |||
Effect of stratification errors on the correlation between treatment groups | When there were no stratification errors, the correlation between the sample means in the two treatment groups was 0.10 for a strong covariate effect (Relationship between the probability of a stratification error and the correlation between the sample means in the intervention and control groups following stratified r... | PMC7614797 | ||
Estimating treatment effects when all stratification errors are discovered | SE | EFFECT INCREASED | All methods of analysis (unadjusted, adjusted for the randomisation strata and adjusted for the true strata) produced unbiased treatment effect estimates across all scenarios (results not shown). However, the methods differed according to other performance measures. In the reference setting when there were no stratific... | PMC7614797 |
Estimating treatment effects when only some stratification errors are discovered | SE | The unadjusted analysis produced unbiased treatment effect estimates but performed poorly on other performance measures in all scenarios where only some stratification errors were discovered, consistent with the scenarios where all stratification errors were discovered (as expected, since this method is unaffected by w... | PMC7614797 | |
Estimating treatment by covariate interaction effects | When all stratification errors were discovered, using the true strata to test for an interaction effect performed well on all measures across all scenarios. This method consistently produced unbiased parameter estimates (Figure Bias in the treatment (Type I error and power (%) for the interaction test across simulation... | PMC7614797 | ||
Sensitivity of results to sample size and covariate prevalence | Reducing the sample size from 1000 to 200 resulted in larger empirical standard errors and reduced power across all methods as expected (results not shown). It had little impact on analyses based on the true strata otherwise and little impact on bias for any method. In contrast, the smaller sample size reduced but did ... | PMC7614797 | ||
EXAMPLE: THE | SE | REGRESSION, SECONDARY | To illustrate how different methods of adjusting for stratification variables perform in a real trial affected by stratification errors, we consider data from the OPTIMISE trial.Following randomisation, obstetric history was obtained from all participants' medical records, which revealed stratification errors for 23 wo... | PMC7614797 |
DISCUSSION | REGRESSION | In this article, we have used simulation studies and an example dataset to explore the impact of misclassification in a stratification variable when analysing continuous outcomes. Building on previous research,With few stratification errors, our simulations revealed little difference in performance between the differen... | PMC7614797 | |
RECOMMENDATIONS FOR PRACTICE | SECONDARY, APPENDIX | Stratification errors can occur in any trial that uses stratified randomisation. We encourage researchers to consider the possibility of stratification errors at all stages of the trial and provide 10 general recommendations for addressing these errors during the design, conduct, analysis and reporting of the trial in ... | PMC7614797 | |
CONCLUSION | Stratification is a useful tool for achieving balanced treatment groups on important baseline covariates, but greater attention should be given to the risk of stratification errors and the implications these errors have for the trial analysis. In most trials, the discovery of stratification errors is unlikely to depend... | PMC7614797 | ||
FUNDING INFORMATION | This research was supported by a Centre of Research Excellence grant from the Australian National Health and Medical Research Council (ID 1171422), to the Australian Trials Methodology (AusTriM) Research Network. BCK and TPM are funded by the UK MRC, grants MC_UU_00004/07 and MC_UU_00004/09. KJL and TRS are supported b... | PMC7614797 | ||
Supporting information | PMC7614797 | |||
ACKNOWLEDGEMENTS | FORBES | The authors would like to thank Professor Andrew Forbes and other members of the Australian Trials Methodology (AusTriM) Research Network for useful feedback on this work. Open access publishing facilitated by The University of Adelaide, as part of the Wiley ‐ The University of Adelaide agreement via the Council of Aus... | PMC7614797 | |
DATA AVAILABILITY STATEMENT | The simulated data used to generate the findings presented in this article can be generated using the Stata code provided as supplementary material. The data from the OPTIMISE trial example are not publicly available due to privacy restrictions but can be requested by contacting the second author. | PMC7614797 | ||
REFERENCES | PMC7614797 | |||
Background | ovulatory dysfunction | The aim of this study was to compare the efficacy of the combination of clomiphene citrate (CC) and letrozole to that of CC alone in inducing ovulation in infertile women with ovulatory dysfunction. | PMC10647029 | |
Methods | Anovulatory infertility | ADVERSE EVENTS, ANOVULATORY INFERTILITY, SECONDARY | A randomized controlled trial was conducted at a single academic medical center between November 2020 and December 2021. Anovulatory infertility females, aged 18 to 40, were evenly distributed by a computer-generated block of four into two treatment groups. A “combination group” received a daily dose of CC (50 mg) and ... | PMC10647029 |
Results | One hundred women (50 per group) were enrolled in the study. The mean age was not significantly different in both groups: 31.8 years in the combination group and 32.4 years in the CC-alone groups ( | PMC10647029 | ||
Conclusions | ovulatory dysfunction | Our study found no significant difference between the combination of CC and letrozole and CC alone in inducing ovulation in infertile women with ovulatory dysfunction in one cycle. The small number of live births precluded any meaningful statistical analysis. Further studies are needed to validate and extend our findin... | PMC10647029 | |
Trial registration | The study was registered at | PMC10647029 | ||
Supplementary Information | The online version contains supplementary material available at 10.1186/s12905-023-02773-7. | PMC10647029 | ||
Keywords | PMC10647029 | |||
Background | anovulation, Polycystic Ovary Syndrome II, PCOS, infertility, Infertility | INFERTILITY, ANOVULATION | Infertility is defined as the inability to conceive through regular intercourse without contraception for 12 months in women under 35 and 6 months in women 35 or older [Medical induction of ovulation is a primary treatment for anovulation, particularly in patients with PCOS. Clomiphene citrate (CC), a selective estroge... | PMC10647029 |
Methods | PMC10647029 | |||
Study design and overview | Infertility | INFERTILITY | This study employed a randomized controlled trial design. It was conducted at the Infertility and Reproductive Biology Unit of the Department of Obstetrics and Gynecology, Faculty of Medicine Siriraj Hospital, Mahidol University, Bangkok, Thailand, from November 2020 to December 2021. Eligible participants were randoml... | PMC10647029 |
Randomization and blinding | ONCOLOGY, WITHDRAWAL BLEEDING | The randomization scheme in this study was computer-generated using blocks of four, with group assignments concealed in sealed envelopes. The sonographer was blinded to the assignments. Participants were randomized in a 1:1 ratio to receive a daily dosage of CC (50 mg; Ovamit, Remedica Ltd, Limassol, Cyprus) in combina... | PMC10647029 | |
Study procedures | ADVERSE EFFECTS | At the start of their menstrual cycle, participants were instructed to contact the investigator to arrange the ovulation induction schedule. The allocated treatment medication regimen was taken from days 3 through 7 of one menstrual cycle. Home urinary luteinizing hormone (LH) tests were performed twice daily, in the m... | PMC10647029 | |
Outcome measures | The primary outcome was the ovulation rate, defined as a mid-luteal progesterone level greater than 3 ng/mL [ | PMC10647029 | ||
Sample size calculation and statistical analyses | The sample size calculation was informed by Meija et al. [The statistical analyses were performed using PASW Statistics for Windows, version 18.0 (SPSS Inc, Chicago, IL, USA). The study conducted an intention-to-treat analysis involving all randomized participants and a per-protocol analysis restricted to those who fol... | PMC10647029 | ||
Discussion | Abdominal bloating, congenital anomalies, PCOS, infertility, ovulatory dysfunction | ADVERSE EFFECTS, MINOR, ANOVULATORY, SIDE EFFECT | This study aimed to compare the efficacy of a combination of CC and letrozole versus CC alone for ovulation induction in infertile women with ovulatory dysfunction. A previous study reported that women with PCOS had a significantly higher ovulation rate with combination therapy than with letrozole alone [From a theoret... | PMC10647029 |
Conclusions | ovulatory dysfunction | Our study found no significant difference in the ovulation rates of infertile women with ovulatory dysfunction. Specifically, the rates achieved with a combination of CC and letrozole were not significantly different from those achieved with CC 50 mg alone in one cycle. However, the low number of live births precluded ... | PMC10647029 | |
Acknowledgements | The authors gratefully acknowledge the patients who generously agreed to participate in this study and Dr Sasima Tongsai for her assistance with the statistical analyses. | PMC10647029 | ||
Authors’ contributions | P.C. was responsible for project development, data collection, and manuscript editing. S.T. was responsible for manuscript writing and data analysis. I.T. and P.L. were responsible for manuscript review and editing. All authors read and approved the final manuscript. | PMC10647029 | ||
Funding | This study was supported by a grant from the Siriraj Research Development Fund of the Faculty of Medicine Siriraj Hospital, Mahidol University, Bangkok, Thailand. | PMC10647029 | ||
Data availability | The datasets used and/or analyzed during the current study are not publicly available due to the confidentiality of participants’ data and the difficulty of organizing the raw data to be suitable for publication; however, they are available from the corresponding author on reasonable request. | PMC10647029 | ||
Declarations | PMC10647029 | |||
Ethics approval and consent to participate | The study was approved by the Siriraj Institutional Review Board and all participants provided written informed consent to participate before enrollment. All methods were performed in accordance with the Declaration of Helsinki. | PMC10647029 | ||
Consent for publication | Not applicable. | PMC10647029 | ||
Competing interests | The authors declare no competing interests. | PMC10647029 | ||
Conflict of interest | All authors declare that there are no personal or professional conflicts of interest and no financial support from the companies that produce and/or distribute the drugs, devices, or materials described in this report. | PMC10647029 | ||
References | PMC10647029 | |||
Backgroud | malignancy, gastric gastrointestinal stromal tumors, GISTs | REGRESSION | To predict the malignancy of 1–5 cm gastric gastrointestinal stromal tumors (GISTs) by machine learning (ML) on CT images using three models - Logistic Regression (LR), Decision Tree (DT) and Gradient Boosting Decision Tree (GBDT). | PMC10327391 |
Methods | 231 patients from Center 1 were randomly assigned into the training cohort (n = 161) and the internal validation cohort (n = 70) in a 7:3 ratio. The other 78 patients from Center 2 served as the external test cohort. Scikit-learn software was used to build three classifiers. The performance of the three models were eva... | PMC10327391 | ||
Results | GBDT outperformed LR and DT with the largest AUC values (0.981 and 0.815) in the training and internal validation cohorts and the greatest accuracy (0.923, 0.833 and 0.844) across all three cohorts. However, LR was found to have the largest AUC value (0.910) in the external test cohort. DT yielded the worst accuracy (0... | PMC10327391 | ||
Conclusions | GISTs | ML classifiers, especially GBDT and LR with high accuracy and strong robustness, were considered to be promising in risk classification of 1–5 cm gastric GISTs based on CT. Long diameter was found the most important feature for risk stratification. | PMC10327391 | |
Supplementary Information | The online version contains supplementary material available at 10.1186/s12880-023-01053-y. | PMC10327391 | ||
Keywords | PMC10327391 | |||
Backgroud | gastric mesenchymal tumors, tumor, neoplasms, hemorrhage, GISTs, necrosis, [Machine learning | TUMOR, ULCERATION, GASTROINTESTINAL STROMAL TUMORS, NEOPLASMS, DISEASE, HEMORRHAGE, GIST, NECROSIS, ONCOLOGY, METASTASES | Gastrointestinal stromal tumors (GISTs) are neoplasms that arise from Cajal cells of the gastrointestinal tract mesenchyme [The National Institutes of Health (NIH) classification system has been proposed to stratify the risk of GISTs. Currently, the modified NIH risk stratification criteria and the latest Chinese conse... | PMC10327391 |
Methods | PMC10327391 | |||
Patient selection | gastric GISTs, tumor | TUMOR | This retrospective study was approved by the ethics committee of Tongde Hospital of Zhejiang Province and the need for informed consent was waived (Approval No. 2021-040). Patients with gastric GISTs from two centers (Center 1: Tongde Hospital of Zhejiang Province, Center 2: Zhejiang Hospital) from January, 2012 to Sep... | PMC10327391 |
CT examination | All patients underwent abdominal CE-CT examination using two 64-slice spiral CT scanners (Siemens, Forchheim, Germany or Philips Medical Systems, Cleveland, OH, USA). The parameters of CT imaging were set as follows: for Siemens, 120 kV tube voltage, 150–250 mA tube current, 0.5 s tube rotation time, 64 × 0.6 mm detect... | PMC10327391 | ||
Image analysis | tumor, calcification, necrotic, necrosis | NECROSIS, TUMOR, ENLARGED LYMPH NODES, NECROTIC, ULCERATION | Two radiologists (Reviewer 1 with 6 years and Reviewer 2 with 13 years of experience in abdominal imaging) independently reviewed CT images and reached final conclusions by consensus without knowledge of the surgical and pathological information of every patient. The determined CT imaging features included (a) the CT a... | PMC10327391 |
Machine learning | Scikit-learn software was used to build three classifiers-DT, GBDT and LR. The detailed methodology is described on the website of official documentation ( | PMC10327391 | ||
Grid search strategy for selecting optimal parameters | In order to find the optimal parameters of three models, the grid search strategy in scikit-learn software was used. In the grid search process, 5-fold Cross-Validation (CV) was used to evaluate model performance. Meanwhile, the accuracy was used as an evaluation metric to maximize model performance. The detail of grid... | PMC10327391 | ||
Logistic regression (LR) | LR is the most conventional approach to measure the relationship between discrete response variables and several covariates by estimating probabilities. It can be written as: The final optimal parameters of LR were set as follows: C = 100, random_state = 12, penalty = ’l1’, solver = ’liblinear’. Other parameter factors... | PMC10327391 | ||
Decision tree (DT) | DT, as a binary method, can be used to classify data by calculating their characteristics. Decision nodes, branches and leaves are the three main components of DT. DT starts with a node and extends to many branches and child nodes, finally to leaves. The criterion used in our model is the Gini’s Diversity Index, which ... | PMC10327391 | ||
Gradient boosting decision tree (GBDT) | GBDT is an ensemble classifier based on bootstrap sampling, which aims to improve the generalization ability and robustness by combining the prediction results of multiple base learners (i.e. weak decision trees). The weight is adjusted with iteration, so that the higher weight will be assigned to the data poorly class... | PMC10327391 | ||
Performance comparison between radiologists and models | Diagnostic performance differences between the three ML models and the two radiologists were compared in the external test cohort. Before performance comparisons, intra-class correlation coefficients (ICCs) were calculated to assess agreement between the two reviewers. | PMC10327391 | ||
Feature variable analysis | gastric GISTs | GBDT and LR showed excellent diagnostic efficiency in predicting risk classification of gastric GISTs on account of the high accuracy and strong robustness. LR is well known for determining the beneficial features to support decision by linear analysis, since the result is easy to explain. Firstly, significant CT featu... | PMC10327391 | |
Statistical analysis | high-grade malignancy, malignancy, SD | P-P plots and Q-Q plots were used to assess normal distribution of data. Continuous distributed data were showed as mean ± SD, and categorical variables were expressed as n (%). Univariate analysis using t test or Mann-Whitney U test for continuous variables and Fisher’s exact test for categorical variables were perfor... | PMC10327391 | |
Results | PMC10327391 | |||
Clinical characteristics of patients | gastric GISTs, GISTs, tumors | TUMORS | 231 patients (109 men and 122 women; mean age, 59.47 ± 10.13 years) from Center 1 and 78 patients (41 men and 37 women; mean age, 62.69 ± 10.78 years) from Center 2 were included in our series. The training cohort enrolled 161 patients with gastric GISTs consisting of 47 high-risk tumors and 114 low-risk ones. 70 patie... | PMC10327391 |
Model evaluation | gastric GISTs, Confusion | REGRESSION | Results of diagnostic performance of LR, DT and GBDT are described in Table
Diagnostic performance analysis of LR, DT and GBDT modelsLR, Logistic regression; DT, Decision tree; GBDT, Gradient boosting decision tree; AUC, area under the curve; CI, confidence interval, NPV, negative predictive value; PPV, positive predi... | PMC10327391 |
Performance comparison between radiologists and models | ICC of 0.83 indicated that the agreement between two radiologists was good. Table
Results of radiologists’ diagnostic performance in the external test cohortAUC, area under the curve; CI, confidence interval, NPV, negative predictive value; PPV, positive predictive value | PMC10327391 | ||
Discussion | tumor, GISTs tumors, malignancy, gastric GISTs, GISTs, high-grade malignant GISTs | REGRESSION, TUMOR, GIST | To our best knowledge, this is the first research on the prediction of malignancy in gastric GISTs by machine learning classifiers. In addition, our report focuses on GISTs tumors of 1–5 cm in the gastric, which is different from studies that include large-size GISTs located in various sites of the gastrointestinal tra... | PMC10327391 |
Conclusions | GISTs | In summary, GBDT and LR showed outstanding performance with high accuracy and strong robustness in the risk assessment of gastric GISTs less than 5 cm on CT imaging. The long diameter of lesion was found to be the most important feature for risk stratification. | PMC10327391 | |
Electronic supplementary material | Below is the link to the electronic supplementary material.
Supplementary Material 1
Supplementary Material 2 | PMC10327391 | ||
Acknowledgements | Not applicable. | PMC10327391 | ||
Authors’ contributions | CZ, JW, HJY designed the research. JW, HJY provided the administrative support for this research. CZ, YY, BLD, ZHX, FMZ were responsible for data collection. CZ, YY, BLD, ZHX, FMZ were responsible for data analysis and interpretation. CZ wrote the first draft. All authors reviewed the analyses and drafts of this manusc... | PMC10327391 | ||
Funding | This research was supported by Zhejiang Provincial Natural Science Foundation of China under Grant No.LGF21H030004. | PMC10327391 | ||
Data Availability | All datasets presented and analyzed in this study were interpreted and provided by the corresponding author. | PMC10327391 | ||
Declarations | PMC10327391 | |||
Ethics approval and consent to participate | This retrospective study was approved by ethics committee of Tongde Hospital of Zhejiang Province (Approval No: 2021-040). Written informed consent was waived by the ethics committee of Tongde Hospital of Zhejiang Province. All methods were carried out in accordance with relevant guidelines and regulations. | PMC10327391 | ||
Consent for publication | Not applicable. | PMC10327391 | ||
Competing interests | The authors declare no competing interests. | PMC10327391 |
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