Confidence intervals for the common coefficient of variation of rainfall in Thailand

Characterization of Combined Blast- and Fragment-Induced Pelvic Injuries and Hemostatic Resuscitation in Rabbits

INTRODUCTION

To establish a blast- and fragment-induced pelvic injury animal model in rabbits, observe its injury characteristics, and explore the effects of hemostatic resuscitation combined with damage control surgery (DCS) with respect to this injury model.

METHODS

Forty-eight rabbits were randomly allocated to four groups: group A rabbits were subjected to pelvic injury, group B rabbits to pelvic injury + DCS, group C rabbits to pelvic injury + DCS + resuscitation with Hextend, and group D rabbits to pelvic injury + DCS + Hextend + hemostatic resuscitation with tranexamic acid, fibrinogen concentrate, and prothrombin complex concentrate. Simulated blast and fragment-induced pelvic injury was produced by a custom-made machine. We implemented CT scanning and necropsy to assess the injury state and calculated the coefficient of variation (CV) of the cumulative abbreviated injury scale (AIS) to assess the reproducibility of the animal model. Immediately after instrumentation (0 h), and 1 h, 2 h, 4 h, and 8 h after injury, blood samples were taken for laboratory tests.

RESULTS

We found that severe pelvic injury was produced with an AIS CV value of 10.32%, and the rabbits demonstrated severe physiologic impairment and coagulo-fibrinolytic derangements with high mortality. In rabbits of group D, however, physiologic and coagulo-fibrinolytic parameters were significantly enhanced with improved organ function and lowered mortality when compared with the other three groups.

CONCLUSIONS

We herein established in rabbits a blast- and fragment-induced pelvic injury animal model that exhibited high reproducibility, and we demonstrated that hemostatic resuscitation plus DCS was effective in improving the outcome.

Inter-rater and intra-rater reliability of isotonic exercise monitoring device for measuring active knee extension

Background

The goal of this study was to assess the reliability of electromyography and range of motion measurements obtained using a knee exercise monitoring system. This device was developed to collect data on knee exercise activities.

Methods

Twenty healthy individuals performed isotonic quadriceps exercises in this study. The vastus medialis surface electromyography (sEMG) and range of motion (ROM) of the knee were recorded during the exercise using the isotonic knee exercise monitoring device, the Mobi6-6b, and a video camera system. Each subject underwent a second measuring session at least 24 h after the first session. To determine reliability, the intraclass correlation coefficients (ICCs) and standard error of measurement (SEM) at the 95% confidence interval were calculated, and a Bland-Altman analysis was performed.

Results

For inter-rater reliability, the ICCs of the mean absolute value (MAV) and root mean square (RMS) of sEMG were 0.73 (0.49, 0.86) and 0.79 (0.61, 0.89), respectively. ROM had an ICC of 0.93 (0.02, 0.98). The intra-rater reliability of the MAV of the sEMG was 0.89 (0.71, 0.96) and the intra-rater reliability of RMS of the sEMG was 0.88 (0.70, 0.95). The ROM between days had an intra-rater reliability of 0.82 (0.54, 0.93). The Bland-Altman analysis demonstrated no systematic bias in the MAV and RMS of sEMG, but revealed a small, systematic bias in ROM (-0.8311 degrees).

Conclusion

For sEMG and range of motion measures, the isotonic knee exercise monitoring equipment revealed moderate to excellent inter- and intra-rater agreement. However, the confidence interval of ROM inter-rater reliability was quite large, indicating a small agreement bias; hence, the isotonic knee exercise monitor may not be suitable for measuring ROM. This isotonic knee exercise monitor could detect and collect information on a patient's exercise activity for the benefit of healthcare providers.

Animal model-based simulation training for three emergent and urgent operations of penetrating thoracic injuries

PURPOSE

To develop animal models of penetrating thoracic injuries and to observe the effects of the animal model-based training on improving the trainees' performance for emergent and urgent thoracic surgeries.

METHODS

With a homemade machine, animal models of lung injuries and penetrating heart injuries were produced in porcine and used for training of chest tube drainage, urgent sternotomy, and emergent thoracotomy. Coefficient of variation of abbreviated injury scale and blood loss was calculated to judge the reproducibility of animal models. Five operation teams from basic-level hospitals (group A) and five operation teams from level III hospitals (group B) were included to be trained and tested. Testing standards for the operations were established after thorough literature review, and expert questionnaires were employed to evaluate the scientificity and feasibility of the testing standards. Tests were carried out after the training. Pre- and post-training performances were compared. Post-training survey using 7-point Likert scale was taken to evaluate the feelings of the trainees to these training approaches.

RESULTS

Animal models of the three kinds of penetrating chest injuries were successfully established and the coefficient of variation of abbreviated injury scale and blood loss were all less than 25%. After literature review, testing standards were established, and expert questionnaire results showed that the scientific score was 7.30 ± 1.49, and the feasibility score was 7.50 ± 0.89. Post-training performance was significantly higher in both group A and group B than pre-training performance. Post-training survey showed that all the trainees felt confident in applying the operations and were generally agreed that the training procedure were very helpful in improving operation skills for thoracic penetrating injury.

CONCLUSIONS

Animal model-based simulation training established in the current study could improve the trainees' performance for emergent and urgent thoracic surgeries, especially of the surgical teams from basic-level hospitals.

Estimation of common percentile of rainfall datasets in Thailand using delta-lognormal distributions

Weighted percentiles in many areas can be used to investigate the overall trend in a particular context. In this article, the confidence intervals for the common percentile are constructed to estimate rainfall in Thailand. The confidence interval for the common percentile help to indicate intensity of rainfall. Herein, four new approaches for estimating confidence intervals for the common percentile of several delta-lognormal distributions are presented: the fiducial generalized confidence interval, the adjusted method of variance estimates recovery, and two Bayesian approaches using fiducial quantity and approximate fiducial distribution. The Monte Carlo simulation was used to evaluate the coverage probabilities and average lengths via the R statistical program. The proposed confidence intervals are compared in terms of their coverage probabilities and average lengths, and the results of a comparative study based on these metrics indicate that one of the Bayesian confidence intervals is better than the others. The efficacies of the approaches are also illustrated by applying them to daily rainfall datasets from various regions in Thailand.

Bayesian computation for the common coefficient of variation of delta-lognormal distributions with application to common rainfall dispersion in Thailand

Rainfall fluctuation makes precipitation and flood prediction difficult. The coefficient of variation can be used to measure rainfall dispersion to produce information for predicting future rainfall, thereby mitigating future disasters. Rainfall data usually consist of positive and true zero values that correspond to a delta-lognormal distribution. Therefore, the coefficient of variation of delta-lognormal distribution is appropriate to measure the rainfall dispersion more than lognormal distribution. In particular, the measurement of the dispersion of precipitation from several areas can be determined by measuring the common coefficient of variation in the rainfall from those areas together. Herein, we compose confidence intervals for the common coefficient of variation of delta-lognormal distributions by employing the fiducial generalized confidence interval, equal-tailed Bayesian credible intervals incorporating the independent Jeffreys or uniform priors, and the method of variance estimates recovery. A combination of the coverage probabilities and expected lengths of the proposed methods obtained via a Monte Carlo simulation study were used to compare their performances. The results show that the equal-tailed Bayesian based on the independent Jeffreys prior was suitable. In addition, it can be used the equal-tailed Bayesian based on the uniform prior as an alternative. The efficacies of the proposed confidence intervals are demonstrated via applying them to analyze daily rainfall datasets from Nan, Thailand.