Cat owners do not provide more reliable weight estimates for cats compared with veterinary professionals in an emergency setting.

OBJECTIVES: The aim of the present study was to assess the accuracy of weight estimation in cats provided by pet owners, veterinary technicians, house officers (interns and residents) and attending clinicians in an emergency room (ER). An additional objective was to determine whether carrying the cat contributed to a more accurate weight estimate. METHODS: A total of 72 cats presented to an ER and were enrolled in the study. Pet owners, veterinary technicians, house officers and attending clinicians were asked to record the cats' estimated weights on individual data collection cards. The actual weights of the cats were then obtained and compared with participants' estimations. RESULTS: There were no significant differences between weight estimates provided by pet owners, veterinary technicians, house officers and attending clinicians. Similarly, neither the length of experience of the veterinary staff nor carrying the cat had an effect on the provision of a more accurate weight estimate. CONCLUSIONS AND RELEVANCE: Pet owners are no better at weight estimation of cats in the ER setting compared with veterinary professionals. Carrying the cat did not improve the accuracy of weight estimation among veterinary professionals. The mean cat weight of 4.9 kg could be used as an average cat weight in an emergency situation for an adult cat.

The accuracy of sonographic fetal weight in very preterm infants (≤32 weeks).

OBJECTIVE: To examine the accuracy of sonographic fetal weight to predict birthweight in very preterm infants (<32 weeks), and to compare the accuracy of estimated fetal weight (EFW) between those small for gestational age (SGA) and those appropriate for gestational age (AGA). STUDY DESIGN: A retrospective study was conducted of data recorded between January 2010 and March 2023. Included were women with singleton livebirths at 23+0-31+6 weeks who had an EFW within one week from delivery. Mean percentage error, mean absolute percentage error, and underestimation and overestimation rates were calculated. We compared the accuracy of EFW between SGA and AGA infants. RESULTS: In total, 360 women were included. The mean absolute percentage error was 7.8 % (range 0 %-68.9 %); for 207 (57.5 %) infants the percentage error was within ±10 %. Overestimation error >10 % was observed in 102 (28.3 %) infants and errors >20 % in 34 (9.4 %). Among infants born in the periviable period (23+0 - 25+6 weeks; N = 56), the mean absolute percentage error was 9.8 % (range: 0 %-40.3 %); the value was within ±10 % for only 28 periviable infants (50 %) and exceeded 20 % for 16.1 %. Among SGA compared to AGA infants, the mean absolute percentage error was higher (11.1% vs. 6.6 %, p = 0.035). Overestimation error >10 % was more frequent among SGA than AGA infants (55 (49.1 %) vs. 47 (19.0 %), p < 0.001). In a multivariate logistic regression analysis, SGA status was independently associated with a higher mean percentage error (beta = 0.260, p < 0.001) and an increased risk of an error >10 % (odds ratio = 2.1, 95 % confidence interval 1.2-3.5, p = 0.008). CONCLUSIONS: Sonographic EFW is limited in assessing very preterm infants, particularly those who are SGA or born during the periviable period. These limitations should be considered regarding impending very preterm births and concerns about abnormal fetal growth.

Estimation of Weight and Body Measurement Model for Pigs Based on Back Point Cloud Data.

Pig farming is a crucial sector in global animal husbandry. The weight and body dimension data of pigs reflect their growth and development status, serving as vital metrics for assessing their progress. Presently, pig weight and body dimensions are predominantly measured manually, which poses challenges such as difficulties in herding, stress responses in pigs, and the control of zoonotic diseases. To address these issues, this study proposes a non-contact weight estimation and body measurement model based on point cloud data from pig backs. A depth camera was installed above a weighbridge to acquire 3D point cloud data from 258 Yorkshire-Landrace crossbred sows. We selected 200 Yorkshire-Landrace sows as the research subjects and applied point cloud filtering and denoising techniques to their three-dimensional point cloud data. Subsequently, a K-means clustering segmentation algorithm was employed to extract the point cloud corresponding to the pigs' backs. A convolutional neural network with a multi-head attention was established for pig weight prediction and added RGB information as an additional feature. During the data processing process, we also measured the back body size information of the pigs. During the model evaluation, 58 Yorkshire-Landrace sows were specifically selected for experimental assessment. Compared to manual measurements, the weight estimation exhibited an average absolute error of 11.552 kg, average relative error of 4.812%, and root mean square error of 11.181 kg. Specifically, for the MACNN, incorporating RGB information as an additional feature resulted in a decrease of 2.469 kg in the RMSE, a decrease of 0.8% in the MAPE, and a decrease of 1.032 kg in the MAE. Measurements of shoulder width, abdominal width, and hip width yielded corresponding average relative errors of 3.144%, 3.798%, and 3.820%. In conclusion, a convolutional neural network with a multi-head attention was established for pig weight prediction, and incorporating RGB information as an additional feature method demonstrated accuracy and reliability for weight estimation and body dimension measurement.

Analyzing Data Modalities for Cattle Weight Estimation Using Deep Learning Models.

We investigate the impact of different data modalities for cattle weight estimation. For this purpose, we collect and present our own cattle dataset representing the data modalities: RGB, depth, combined RGB and depth, segmentation, and combined segmentation and depth information. We explore a recent vision-transformer-based zero-shot model proposed by Meta AI Research for producing the segmentation data modality and for extracting the cattle-only region from the images. For experimental analysis, we consider three baseline deep learning models. The objective is to assess how the integration of diverse data sources influences the accuracy and robustness of the deep learning models considering four different performance metrics: mean absolute error (MAE), root mean squared error (RMSE), mean absolute percentage error (MAPE), and R-squared (R2). We explore the synergies and challenges associated with each modality and their combined use in enhancing the precision of cattle weight prediction. Through comprehensive experimentation and evaluation, we aim to provide insights into the effectiveness of different data modalities in improving the performance of established deep learning models, facilitating informed decision-making for precision livestock management systems.

A modified Michaelis-Menten equation estimates growth from birth to 3 years in healthy babies in the USA.

BACKGROUND: Standard pediatric growth curves cannot be used to impute missing height or weight measurements in individual children. The Michaelis-Menten equation, used for characterizing substrate-enzyme saturation curves, has been shown to model growth in many organisms including nonhuman vertebrates. We investigated whether this equation could be used to interpolate missing growth data in children in the first three years of life and compared this interpolation to several common interpolation methods and pediatric growth models. METHODS: We developed a modified Michaelis-Menten equation and compared expected to actual growth, first in a local birth cohort (N = 97) then in a large, outpatient, pediatric sample (N = 14,695). RESULTS: The modified Michaelis-Menten equation showed excellent fit for both infant weight (median RMSE: boys: 0.22 kg [IQR:0.19; 90% < 0.43]; girls: 0.20 kg [IQR:0.17; 90% < 0.39]) and height (median RMSE: boys: 0.93 cm [IQR:0.53; 90% < 1.0]; girls: 0.91 cm [IQR:0.50;90% < 1.0]). Growth data were modeled accurately with as few as four values from routine well-baby visits in year 1 and seven values in years 1-3; birth weight or length was essential for best fit. Interpolation with this equation had comparable (for weight) or lower (for height) mean RMSE compared to the best performing alternative models. CONCLUSIONS: A modified Michaelis-Menten equation accurately describes growth in healthy babies aged 0-36 months, allowing interpolation of missing weight and height values in individual longitudinal measurement series. The growth pattern in healthy babies in resource-rich environments mirrors an enzymatic saturation curve.

Nondestructive estimation method of live chicken leg weight based on deep learning.

In the broiler-breeding industry, phenotype determination is critical. Leg weight is a fundamental indicator for breeding, and noninvasive testing technology can reduce damage to animals. This study proposes a broiler leg weight estimation system comprising a weight-estimation model and computed tomography (CT) acquisition equipment. The weight-estimation model can automatically process the scan results of live broiler chickens from the CT acquisition equipment. The weight-estimation model comprises an improved you-only-look-once (YOLOv5) segmentation algorithm and a random forest fitting network. The segmentation head was introduced into the YOLOv5 network, combined with a multiscale attention mechanism and an atrous spatial pyramid pooling architecture, and a new network model, YOLO- measuring chicken leg weight (YOLO-MCLW), was proposed to improve segmentation efficiency and accuracy. Morphological parameters were extracted from the obtained mask image, and a random forest network was used for fitting. The experiments show that the system exhibited an average absolute error of 7.27 g and an average percentage error of 4.82% in tests on 50 individual legs of 25 broiler chickens. The prediction R2 of broiler chicken legs can reaches 88.98%, the segmentation intersection over union result reaches 95.45%, and 37.04 images are processed per second. This system provides technical support for the part determination of broiler chickens in commercial breeding.

Does decreased fetal growth estimation in the appropriate for gestational age range affect delivery outcomes?

PURPOSE: To study the effect of decreased estimated fetal weight (EFW) percentiles in appropriate for gestational age fetuses. METHODS: This retrospective cohort study included women who had second and third trimester ultrasound examinations. Delivery and neonatal outcomes of pregnancies with decreased EFW of ≥ 30 percentiles in EFW between ultrasound examinations (decreased growth group) and those without such a decrease (control group) were compared. Deliveries with EFW or birthweight below the 10th percentile were excluded. RESULTS: Among 1610 deliveries, 57 were in the decreased growth group and 1553 in the control group. Maternal characteristics did not differ between the groups except for higher rate of nulliparity in the decreased growth group. We found similar rates of Category II/III monitoring, cesarean deliveries due to non-reassuring fetal heart rate and adverse neonatal outcomes. Neonatal birthweight was lower in the decreased growth group as compared to controls. CONCLUSIONS: This study did not find association between the group of appropriate for gestational age fetuses with decreased growth, with adverse outcomes.

The accuracy of total body weight estimation in adults - A systematic review and meta-analysis.

BACKGROUND: Weight estimation is required in adult patients when weight-based medication must be administered during emergency care, as measuring weight is often impossible. Inaccurate estimations may lead to inaccurate drug doses, which may cause patient harm. Little is known about the relative accuracy of different methods of weight estimation that could be used during resuscitative care. The aim of this study was to evaluate the performance and suitability of existing weight estimation methods for use in adult emergency care. METHODS: A systematic literature search was performed for suitable articles that studied the accuracy of weight estimation systems in adults. The study characteristics, the quality of the studies, the weight estimation methods evaluated, the accuracy data, and any information on the ease-of-use of the method were extracted and evaluated. RESULTS: A total of 95 studies were included, in which 27 different methods of total body weight estimation were described, with 42 studies included in the meta-analysis. The most accurate methods, determined from the pooled estimates of accuracy (the percentage of estimates within 10% of true weight, with 95% confidence intervals) were 3-D camera estimates (88.8% (85.8 to 91.8%)), patient self-estimates (88.7% (87.7 to 89.7%)), the Lorenz method (77.5% (76.4 to 78.6%)) and family estimates (75.0% (71.5 to 78.6%)). However, no method was without significant potential limitations to use during emergency care. CONCLUSION: Patient self-estimations of weight were generally very accurate and should be the method of choice during emergency care, when possible. However, since alternative estimation methods must be available when confused, or otherwise incapacitated, patients are unable to provide an estimate, alternative strategies of weight estimation should also be available.

An Improved Method for Broiler Weight Estimation Integrating Multi-Feature with Gradient Boosting Decision Tree.

Broiler weighing is essential in the broiler farming industry. Camera-based systems can economically weigh various broiler types without expensive platforms. However, existing computer vision methods for weight estimation are less mature, as they focus on young broilers. In effect, the estimation error increases with the age of the broiler. To tackle this, this paper presents a novel framework. First, it employs Mask R-CNN for instance segmentation of depth images captured by 3D cameras. Next, once the images of either a single broiler or multiple broilers are segmented, the extended artificial features and the learned features extracted by Customized Resnet50 (C-Resnet50) are fused by a feature fusion module. Finally, the fused features are adopted to estimate the body weight of each broiler employing gradient boosting decision tree (GBDT). By integrating diverse features with GBTD, the proposed framework can effectively obtain the broiler instance among many depth images of multiple broilers in the visual field despite the complex background. Experimental results show that this framework significantly boosts accuracy and robustness. With an MAE of 0.093 kg and an R2 of 0.707 in a test set of 240 63-day-old bantam chicken images, it outperforms other methods.

Validation study on a prediction formula to estimate the weight of children & adolescents with special needs aged 2-18 years old.

BACKGROUND: This study aims to validate two predictive formulas of weight estimating strategies in children with special needs, namely the Cattermole formula and the Mercy formula. METHODOLOGY: A cross-sectional study with a universal sampling of children and adolescents with special needs aged 2-18 years old, diagnosed with cerebral palsy, down syndrome, autism and attention-deficit/hyperactivity disorder was conducted at Community-Based Rehabilitation in Central Zone Malaysia. Socio-demographic data were obtained from files, and medical reports and anthropometric measurements (body weight, height, humeral length, and mid-upper arm circumference) were collected using standard procedures. Data were analysed using IBM SPSS version 26. The accuracy of the formula was determined by intraclass correlation, prediction at 20% of actual body weight, residual error (RE) and root mean square error (RMSE). RESULT: A total of 502 children with a median age of 7 (6) years were enrolled in this study. The results showed that the Mercy formula demonstrated a smaller degree of bias than the Cattermole formula (PE = 1.97 ± 15.99% and 21.13 ± 27.76%, respectively). The Mercy formula showed the highest intraclass correlation coefficient (0.936 vs. 0.858) and predicted weight within 20% of the actual value in the largest proportion of participants (84% vs. 48%). The Mercy formula also demonstrated lower RE (0.3 vs. 3.6) and RMSE (3.84 vs. 6.56) compared to the Cattermole formula. Mercy offered the best option for weight estimation in children with special needs in our study population.

Is there evidence that length-based tapes with precalculated drug doses increase the accuracy of drug dose calculations in children? A systematic review.

Objective: The use of pediatric length-based weight estimation tapes with precalculated drug doses is advocated by major advanced life support organizations, but concerns have been raised on the accuracy of these systems. The objective of this systematic review was to collect, review, evaluate, and create a synthesis of the current literature to establish whether there is high-quality evidence for use of lengthbased tapes in accurate drug dose administration. A further objective was to determine how these tapes compare to other dosing aids. Methods: Eligible studies were identified and analyzed if they were peer reviewed, full text articles containing original data. Studies including any form of length-based precalculated drug dosing methodology in children aged 0 to 18 years were included. Results: Eighteen studies met the inclusion criteria. The most studied of the tapes was the Broselow tape in 16 studies (88.9%). When these tapes were used on their own without additional reference material, they produced a substantial number of potentially harmful dosing errors (>20% error). No tape was superior to another. Using the tapes was better than using no dosing aid but was inferior to using both comprehensive drug dosing guides and novel color-coded medication administration systems. Conclusion: There was no high-quality evidence that the use of length-based tapes with precalculated drug doses leads to accurate drug dosing. However, comprehensive drug dosing systems were more effective at reducing dosing errors than were length-based tapes on their own. The confounding effect of weight estimation accuracy on drug dosing accuracy has not been sufficiently studied.

An Individualized Machine Learning Approach for Human Body Weight Estimation Using Smart Shoe Insoles.

Rapid significant weight fluctuations can indicate severe health conditions such as edema due to congestive heart failure or severe dehydration that could require prompt intervention. Daily body weighing does not accurately represent the patient's body weight fluctuations occurring within a day. The patient's lack of compliance with tracking their weight measurements is also a predominant issue. Using shoe insole sensors embedded into footwear could achieve accurate real-time monitoring systems for estimating continuous body weight changes. Here, the machine learning models' predictive capabilities for continuous real-time weight estimation using the insole data are presented. The lack of availability of public datasets to feed these models is also addressed by introducing two novel datasets. The proposed framework is designed to adapt to the patient, considering several unique factors such as shoe type, posture, foot shape, and gait pattern. The proposed framework estimates the mean absolute percentage error of 0.61% and 0.74% and the MAE of 1.009 lbs. and 1.154 lbs. for the less controlled and more controlled experimental settings, respectively. This will help researchers utilize machine learning techniques for more accurate real-time continuous weight estimation using sensor data and enable more reliable aging-in-place monitoring and telehealth.

Body Weight Estimation for Pigs Based on 3D Hybrid Filter and Convolutional Neural Network.

The measurement of pig weight holds significant importance for producers as it plays a crucial role in managing pig growth, health, and marketing, thereby facilitating informed decisions regarding scientific feeding practices. On one hand, the conventional manual weighing approach is characterized by inefficiency and time consumption. On the other hand, it has the potential to induce heightened stress levels in pigs. This research introduces a hybrid 3D point cloud denoising approach for precise pig weight estimation. By integrating statistical filtering and DBSCAN clustering techniques, we mitigate weight estimation bias and overcome limitations in feature extraction. The convex hull technique refines the dataset to the pig's back, while voxel down-sampling enhances real-time efficiency. Our model integrates pig back parameters with a convolutional neural network (CNN) for accurate weight estimation. Experimental analysis indicates that the mean absolute error (MAE), mean absolute percent error (MAPE), and root mean square error (RMSE) of the weight estimation model proposed in this research are 12.45 kg, 5.36%, and 12.91 kg, respectively. In contrast to the currently available weight estimation methods based on 2D and 3D techniques, the suggested approach offers the advantages of simplified equipment configuration and reduced data processing complexity. These benefits are achieved without compromising the accuracy of weight estimation. Consequently, the proposed method presents an effective monitoring solution for precise pig feeding management, leading to reduced human resource losses and improved welfare in pig breeding.

The impact of different growth charts on birthweight prediction: obstetrical ultrasound vs magnetic resonance imaging.

BACKGROUND: The estimation of fetal weight by fetal magnetic resonance imaging is a simple and rapid method with a high sensitivity in predicting birthweight in comparison with ultrasound. Several national and international growth charts are currently in use, but there is substantial heterogeneity among these charts due to variations in the selected populations from which they were derived, in methodologies, and in statistical analysis of data. OBJECTIVE: This study aimed to compare the performance of magnetic resonance imaging and ultrasound for the prediction of birthweight using 3 commonly used fetal growth charts: the INTERGROWTH-21st Project, World Health Organization, and Fetal Medicine Foundation charts. STUDY DESIGN: Data derived from a prospective, single-center, blinded cohort study that compared the performance of magnetic resonance imaging and ultrasound between 36+0/7 and 36+6/7 weeks of gestation for the prediction of birthweight ≥95th percentile were reanalyzed. Estimated fetal weight was categorized as above or below the 5th, 10th, 90th, and 95th percentile according to the 3 growth charts. Birthweight was similarly categorized according to the birthweight standards of each chart. The performances of ultrasound and magnetic resonance imaging for the prediction of birthweight <5th, <10th, >90th, and >95th percentile using the different growth charts were compared. Data were analyzed with R software, version 4.1.2. The comparison of sensitivity and specificity was done using McNemar and exact binomial tests. P values <.05 were considered statistically significant. RESULTS: A total of 2378 women were eligible for final analysis. Ultrasound and magnetic resonance imaging were performed at a median gestational age of 36+3/7 weeks, delivery occurred at a median gestational age of 39+3/7 weeks, and median birthweight was 3380 g. The incidences of birthweight <5th and <10th percentiles were highest with the Fetal Medicine Foundation chart and lowest with the INTERGROWTH-21st chart, whereas the incidences of birthweight >90th and >95th percentiles were lowest with the Fetal Medicine Foundation chart and highest with the INTERGROWTH-21st chart. The sensitivity of magnetic resonance imaging with an estimated fetal weight >95th percentile in the prediction of birthweight >95th percentile was significantly higher than that of ultrasound across the 3 growth charts; however, its specificity was slightly lower than that of ultrasound. In contrast, the sensitivity of magnetic resonance imaging with an estimated fetal weight <10th percentile for predicting birthweight <10th percentile was significantly lower than that of ultrasound in the INTERGROWTH-21st and Fetal Medicine Foundation charts, whereas the specificity and positive predictive value of magnetic resonance imaging were significantly higher than those of ultrasound for all 3 charts. Findings for the prediction of birthweight >90th percentile were close to those of birthweight >95th percentile, and findings for the prediction of birthweight <5th percentile were close to those of birthweight <10th percentile. CONCLUSION: The sensitivity of magnetic resonance imaging is superior to that of ultrasound for the prediction of large for gestational age fetuses and inferior to that of ultrasound for the prediction of small for gestational age fetuses across the 3 different growth charts. The reverse is true for the specificity of magnetic resonance imaging in comparison with that of ultrasound.

Sensorimotor expertise influences perceptual weight judgments during observation of a sport-specific gesture.

This study aimed to investigate the role of sensorimotor expertise in evaluating relative weight of a lifted object during the observation of a sport-specific gesture, namely the deadlift. Fifty-six participants, assigned to three groups according to their experience in weight lifting, powerlifters, CrossFit® practitioners and naïve participants (controls), performed a perceptual weight judgments task. Participants observed videos showing a powerlifter executing a deadlift at the 80%, 90% and 100% of 1 repetition maximum (1RM) and answered a question about the weight of the lifted object. Participants' response accuracy and variability were evaluated. Findings showed that powerlifters were more accurate than controls. No differences appeared between powerlifter and CrossFit® practitioners, and between CrossFit® practitioners and controls. Response variability was similar in the three groups. These findings suggest that a fine sensorimotor expertise specific for the observed gesture is crucial to detect the weight of the object displayed in the observed movement, since it might allow detecting small changes in the observed movement kinematics, which we speculate are at the basis of the object weight recognition.

Development and Validation of a Weight Estimation Tool for Acutely Ill Children Who Cannot be Weighed.

OBJECTIVES: To develop and validate weight estimation tools using mid upper arm circumference (MUAC) and body length, and determine accuracy and precision of Broselow tape in children 6 mo to 15 y of age. METHODS: Data of 18,456 children aged 6 mo to 5 y and 1420 children aged 5 to 15 y were used to develop linear regression equations using length and MUAC to estimate weight. These were validated on prospectively enrolled populations of 276 and 312 children, respectively. Accuracy was measured by Bland-Altman bias, median percentage errors, and percentage of predicted weight within 10% of true weight. Broselow tape was tested on the validation population. RESULTS: Gender specific equations were developed which estimated weight within 10% of true weight in 69.9% (64.1-75.2%) and 65.7% (60.1-70.9%) of children aged 6 mo to 5 y, and 5 to 15 y, respectively. Broselow tape predicted weight within 10% of the true weight in 40.5% (34.7-46.6%) and 32.5% (26.7-38.7%) of children aged 6 mo to 5 y and 5 to 15 y, respectively. CONCLUSIONS: The model developed from MUAC and length accurately estimated weight in children aged 6 mo to 15 y, and is potentially useful during emergencies. The Broselow tape frequently overestimated weight in authors' setting.

Pet owners provide a more reliable weight estimate for dogs compared to veterinary professionals in an emergency setting.

OBJECTIVE: To determine the accuracy of pet owners, veterinary technicians, house officers (interns and residents), and attending clinicians at estimating dog weights in a veterinary emergency room. ANIMALS: 272 dogs weighing over 2 kg presenting to the emergency room between June 1 and July 29, 2022. METHODS: Pet owners, veterinary technicians, house officers, and attending clinicians recorded the dogs' weight estimations on individual data collection cards. Pet owners were also asked to estimate their dogs' weight during the triage period. The dogs' actual weights were then obtained and recorded. RESULTS: Pet owners were more accurate than veterinary professionals at providing weight estimates for dogs. Weight estimates were accurate to within 10% of the dogs' actual weights for 67.9% (181/267) of pet owners. Forty-one percent (112/270) of attending clinicians, 35.3% (95/269) of house officers, and 35.4% (96/271) of veterinary technicians' weight estimates were within 10% of the dog's actual weight. There was no difference noted in the length of veterinary experience and ability to closely estimate the patient's weight. Overall, veterinary professionals were more likely to closely estimate the weight of large dogs compared to small dogs. CLINICAL RELEVANCE: The pet owner is most likely to provide an accurate weight for dogs and questions about the dog's weight should be directed to the client for situations in which a weight cannot be rapidly obtained.

Weight Estimation for Drug Dose Calculations in the Prehospital Setting - A Systematic Review.

BACKGROUND: Weight estimation is required to enable dose calculations for weight-based drugs administered during emergency care. The accuracy of the estimation will determine the accuracy of the administered dose. This is an important matter of patient safety. The objective of this systematic review was to collect, review, evaluate, and create a synthesis of the current literature focusing on the accuracy of weight estimation in the prehospital environment. METHODS: This systematic review followed the PRISMA guidelines. Studies were identified and included if they were peer reviewed, full length, published in English, and contained original data. Studies utilizing any form of weight estimation methodology in the prehospital setting (in children or adults) were included. Data on the quality of the studies and accuracy of the weight estimation systems were extracted. Common themes were also identified. RESULTS: Twenty-five studies met the inclusion criteria, with only nine studies (36.0%) containing useful weight estimation accuracy data. The overall quality of the studies was poor. The Broselow tape and paramedic estimates were the most studied methods of weight estimation, but there was insufficient evidence to support conclusions about accuracy. The major themes identified included the importance of accurate weight estimation and drug dosing as critical matters of patient safety, and the need for training to ensure these processes are performed accurately. CONCLUSIONS: There were limited robust data identified on the accuracy of different weight estimation methods used in the prehospital setting. Future high-quality clinical research in this area is of critical importance to ensure patient safety in the prehospital environment.

Farmers' participation into the recovery of waste agricultural plastic film: An application of the Theory of Planned Behavior.

This study aims to address the lack of relevant researches in the field of waste recycling using the Theory of Planned Behavior (TPB). A village-scale social survey was conducted to investigate the degree of farmers' participation in a waste plastic film program, i.e. Old for New in northwest China. The program required farmers to recycle plastic film residues in exchange for new films. Survey results showed that 67.5% of farmers accepted the program, yet only 14.5% of them actually participated. Logistic regression analysis was used to analyze questionnaire data and identify the factors that significantly affected farmers' recycling behavior. Principal component and weight analysis further showed that farmers' participation was mainly influenced by their attitudes (p < 0.01), with a relative weight (RW) of 46.3%. Yet, subjective norms (p < 0.1) and perceived behavior control (p < 0.1) had less effect on the degree of participation, and their RWs were 4.2% and 4.1% only, respectively. Moreover, the RW of plastic film usage characteristics and household characteristics reached up to 13.2% and 6.4%, respectively. Interestingly, environmental awareness (ß = 0.083) and compulsory environmental education (ß = 0.130) as surface factors strongly affected the farmers' adoption and response, with the RW of 25.7%. As such, the extended TPB model was established to analyze the participation behavior of farmers for stronger explanatory power. This study highlighted a promising strategy based on TPB for waste plastic film recycling and similar environmental management practices.

Evaluating the accuracy of sonographic fetal weight estimations using the Hadlock IV formula in a Chinese population.

Background: Despite being the most generalized formula in China, the Hadlock IV formula has never been examined to determine if it is suitable for Chinese newborns, nor have the factors that might affect its performance been investigated. However, previous studies have reported varying results about other formulas in other nationalities. This study sought to evaluate the performance of the Hadlock IV formula in estimating fetal weight (FW) in pregnant Chinese women and use ultrasound to identify the factors affecting the accuracy of estimations of newborn weight; through these means, we aimed to create a reference for predicting neonatal weight for obstetricians. Methods: A retrospective observational study comprising data from 976 cases of live-birth singleton pregnancies at the Shanghai General Hospital was conducted. The participants' clinical data were examined and subjected to a logistic regression analysis to identify the multitude of possible factors affecting the estimation of FW. The proportions and correlations between the accurate and inaccurate estimation groups were compared to determine the different prognosis of these 2 groups. The correlations between the accuracy of the sonographic-based fetal weight estimation (SFWE) and newborns with different weight ranges were also analyzed. Results: The overall accuracy rate of the SFWE predicted by the Hadlock IV formula was 79.61%, while that of the inaccurate estimation group was only 20.39%. The incidence of spontaneous vaginal delivery (VD) was lower in the inaccurate estimation group than in the accurate estimation group (40.7% vs. 48.13%; P=0.041). In the inaccurate estimation group, 11.56% (23/199) of the participants underwent a secondary cesarean section (sCS), compared to only 6.44% (50/777) in the accurate estimation group. The low birth weight (LBW) rates and macrosomia rates were lower in the accurate estimation group than in the inaccurate estimation group, with odds ratios (ORs) of 0.483 and 0.459, respectively (P<0.05). The results indicated that the SFWE was more accurate for newborns weighing 2,500-4,000 g than those weight out of this range. In relation to macrosomia, the SFWE was likely to be underestimated, but it was usually overestimated in the LBW group. Conclusions: The overall performance of the Hadlock IV formula in predicting the birth weight of Chinese newborns remains suboptimal. Extra caution should be exercised in cases of suspected large-for-gestational age (LGA) infants, small-for-gestational age (SGA) infants, infants with macrosomia, or LBW fetuses in the Chinese population.