Computer-assisted evaluation of ultrasonic fetal weight prediction using multiple regression equations with and without the fetal femur length.

1985 ◽  
Vol 4 (2) ◽  
pp. 65-67 ◽  
Author(s):  
J S Woo ◽  
C W Wan ◽  
K M Cho
Keyword(s):  
Multiple Regression ◽  
Fetal Weight ◽  
Computer Assisted ◽  
Regression Equations ◽  
Femur Length ◽  
Weight Prediction

Use ofin-vivomeasurements to estimate breast and abdominal fat content of a free-range broiler strain

2003 ◽  
Vol 77 (1) ◽  
pp. 23-31 ◽  
Author(s):  
J. E. Melot ◽  
M. M. Motter ◽  
L. R. Morao ◽  
M. J. Huguet ◽  
Z. Canet ◽  
...  
Keyword(s):  
Multiple Regression ◽  
Live Weight ◽  
Abdominal Fat ◽  
Regression Equations ◽  
Ultrasound Method ◽  
Ultrasound Measurements ◽  
Weight Prediction ◽  
Stepwise Procedure ◽  
Breast Weight

AbstractThe objective of this work was to evaluate the ultrasound method as a tool forin-vivobreast and abdominal fat prediction on Campero-INTA broilers. Breast length (mm), width (mm), surface (mm2; ultrasound) and depth (mm; ultrasound), and abdominal fat depth (mm; ultrasound) were measured at 65-66 and 72-73 days of age on 96 males. At 79 days of age, the broilers were weighed and slaughtered. Breast and abdominal fat weights (g) were obtained. Multiple regression equations were fitted using live weight (g) andin-vivomeasurements to predict breast and abdominal fat weights and proportions (g/g). The best model for each case was selected by the Stepwise procedure. Equations fitted were verified using another set of data. Breast weight prediction using live weight and breast length in the model was as accurate as using live weight and breast depth. The former is recommended for breeding work. Abdominal fat weight prediction using ultrasound measurements of fat was less accurate than breast weight prediction. Repeatabilities for ultrasound breast measurements were higher (0·72 to 0·73) than those for abdominal fat (0·51 to 0·52). Operator effect may be important when training levels are different.

Estimation de la production de jeunes plantations de pin rouge et de pin gris du sud du Québec

1976 ◽  
Vol 6 (4) ◽  
pp. 478-486 ◽  
Author(s):  
H. A. Bolghari
Keyword(s):  
Multiple Regression ◽  
Jack Pine ◽  
Red Pine ◽  
Pine Plantations ◽  
Regression Equations ◽  
Pine Plantation ◽  
South Shore ◽  
Pine Stands ◽  
Natural Stands ◽  
St Lawrence River

Multiple regression equations have been developed to predict yield from young red pine and jack pine plantations. Data from 446 sample plots representing young red pine and jack pine stands located on the south shore of the St. Lawrence River between Quebec and Montreal were analysed. The red pine plantation yielded more than the jack pine. However, in plantation both species yield more than in natural stands. Taking into account the age and spacing of the sampled plantations, the equation obtained can provide information on yield of red pine and jack pine stands the maximum spacing of which is 3 × 3 m, up to the age of 45 and 35 years respectively. The equations will allow the construction of preliminary yield tables for both species.

FAT THICKNESS MEASURED WITH THREE ULTRASONIC INSTRUMENTS ON LIVE RAM LAMBS AS PREDICTORS OF CUTABILITY

1980 ◽  
Vol 60 (4) ◽  
pp. 857-867 ◽  
Author(s):  
ANDRÉ FORTIN
Keyword(s):  
Multiple Regression ◽  
Live Weight ◽  
Optimal Location ◽  
Regression Equations ◽  
Longissimus Muscle ◽  
Fat Thickness ◽  
Ram Lambs

Fat thickness at four locations over the longissimus muscle was measured ultrasonically on 33 live ram lambs ranging in live weight from 16.0 to 37.0 kg. Simple and multiple regression equations were developed to assess the effectiveness of fat thickness as measured by three different ultrasonic instruments (Krautkrämer USM #2, Scanoprobe Model 731A and Scanogram Model 722) to predict cutability. Weight of trimmed or boneless cuts (shoulder + loin + rack + leg) was predicted with more precision than percentage of cuts. Fat thickness alone or combined with weight at scanning was of no significant value (P > 0.05) in the prediction of percentage of trimmed cuts. Percentage of boneless cuts was predicted more efficiently from weight at scanning alone than from fat thickness alone or combined with weight at scanning. Weight of cuts (trimmed or boneless) was also estimated from the fat measurement (P < 0.01), the weight at scanning (P < 0.01) or a combination of both variables. For the latter, fat thickness did not contribute significantly (P > 0.05). The optimal location of the fat measurement depended on the ultrasonic instrument used. Fat thickness measured with the Krautkrämer was more efficient in its prediction of cutability than fat thickness measured with the Scanoprobe or Scanogram. However, over the range of liveweights studied, the usefulness of fat thickness measured on live ram lambs to predict cutability is questionable.

scholarly journals Accuracy of ultrasonic fetal weight estimation using head and abdominal circumference and femur length

2005 ◽  
Vol 58 (11-12) ◽  
pp. 548-552 ◽  
Author(s):  
Ljiljana Mladenovic-Segedi ◽  
Dimitrije Segedi
Keyword(s):  
Birth Weight ◽  
Head Circumference ◽  
Fetal Weight ◽  
Abdominal Circumference ◽  
Weight Estimation ◽  
Fetal Head ◽  
Total Body Mass ◽  
Femur Length ◽  
Fetal Weight Estimation ◽  
Gynecology And Obstetrics

Introduction Former investigations have shown that the accuracy of fetal weight estimation is significantly higher if several ultrasonic fe?tal parameters are measured, because the total body mass depends on the size of fetal head, abdominal circumference and femur length. The aim of this investigation was to establish the best regression model, that is a number of combinations of fetal parameters providing the most accurate fetal weight estimation in utero in our population. Material and methods This prospective study was carried out at the Gynecology and Obstetrics Clinic of the Clinical Center Novi Sad. It included 270 pregnant women with singleton pregnancies within 72 hours of delivery who underwent ultrasound measurements of the biparietal diameter (BPD), head circumference (HC), ab?dominal circumference (AC) and femur length (FL). Results In regard to fetal weight estimation formulas, the deviation was lowest using regression models that simultaneously analyzed four fetal parameters (0.55%) with SD ?7.61%. In these models the estimates of fetal weights were within ?5% of actual birth weight in 48.89%, and within ?10% of actual birth weight in 81.48%. Good results were also obtained using AC, FL measurements (0.92% ? 8.20) as well as using AC, HC, FL measurements (-1.45% ? 7.81). In our sample the combination of AC and FL model gave better results in fetal weight estimation (0.92 ? 8.20%) than the one using BPD and AC (2.97 ? 8.83%). Furthermore, the model using parameters AC, HC and FL showed a lower error in accuracy (-1.45 ? 7.81%) than the model using BPD, AC and FL (2.51 ? 7.82%). Conclusion This investigation has confirmed that the accuracy of fetal weight estimation increases with the number of measured ultra?sonic fetal parameters. In our population the greatest accuracy was obtained using BPD, HC, AC and FL model. In cases when fast estimation of fetal weight is needed, AC, HC, FL model may be appropriate, but if fetal head circumference cannot be measured (amnion rupture and/or fetal head already in the pelvis) the AC, FL model should be used.

Femur length in the estimation of fetal weight less than 1500 grams

1984 ◽  
Vol 149 (2) ◽  
pp. 233-235 ◽  
Author(s):  
John W. Seeds ◽  
Robert C. Cefalo ◽  
Watson A. Bowes
Keyword(s):  
Fetal Weight ◽  
Femur Length

EQUATIONS FOR RECIPROCAL CONVERSIONS OF STARCH EQUIVALENT AND TOTAL DIGESTIBLE NUTRIENT VALUES

1962 ◽  
Vol 42 (2) ◽  
pp. 211-214
Author(s):  
J. Kielanowski ◽  
Aleksandra Ziolecka ◽  
Zofia Osińska
Keyword(s):  
Multiple Regression ◽  
Fiber Content ◽  
Crude Fiber ◽  
Regression Equations ◽  
Crude Fiber Content ◽  
Independent Variable ◽  
Total Digestible Nutrient

In order to facilitate reciprocal conversions of Starch Equivalents (SE) and Total Digestible Nutrients (TDN) values of feeds, multiple regression equations were computed for three different groups of feeds (concentrates, green roughages and silages, and dry roughages. These equations are: for concentrates, green roughages and silages, and dry roughages, respectively.[Formula: see text]Where Y = percentage TDN, X1 = percentage SE, and X2 = percentage crude fiber and[Formula: see text]Where Y = percentage SE; X1 = percentage TDN, and X2 = percentage crude fiber.The addition of the crude fiber content as the second independent variable in each equation resulted in a slight but marked gain in accuracy, especially for green and dry roughages, when compared with simple regressions of TDN on SE or vice versa.

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