Prediction of Whole-Body and Segmental Body Composition by Bioelectrical Impedance in Morbidly Obese Subjects
Open accessAbstract
Background Validated equations for body composition analysis using bioelectrical impedance (BIA) in morbidly obese (MO) subjects are scarce. Thus, our aim was todevelop new equations from physical and BIA parameters to estimate whole-body and segmental body composition inMO subjects, with dual-energy X-ray absorptiometry(DXA) as the reference method.Methods A cross-sectional study on 159 Caucasian MO subjects (female 78%, age 43.5 ± 11.8 years, BMI 45.6 ± 4.9 kg/m2) divided in two groups was conducted: model building cohort (n = 110) and model validation cohort (n 0 49). Stepwise regression analysis was used to develop specific fat free mass (FFM) and fat mass (FM) equations.Results Gender, body weight, and height2/impedance accounted, respectively, for 89.4% (p < 0.001) and 89.3% (p < 0.001) of the variability of DXA-total FFM in the two cohorts. Using the new equation, the mean difference between the DXA-FFM and BIA-FFM estimates was +0.180 kg (95% CI: -0.34 to +0.7 kg, p 0 NS), and the resulting limits of agreement were +6.76 and −6.40 kg. Similarly, good estimates of DXA truncal-, android-, and gynoid-FM from anthropometric and BIA parameters could be obtained from weight, height2/impedance, and waist and hip circumferences (respectively, R2 adjusted: 0.657, 0.776, and 0.770; p < 0.001).Conclusions The new equations derived from physical and BIA parameters provide accurate estimates of body composition in MO subjects.
Integrity signals
Registry
Cites
Statistics
Claims the paper makes
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
Risk of bias was high in two studies.
“Risk of bias was high in two [34, 35] and unclear in six [36–41] studies due to the low to moderate (R2 < 80%) coefficient of determination (R2) resulted from the multiple regression models.”
From High-standard predictive equations for estimating body composition using bioelectrical impedance analysis: a systematic review (2024) · cites this paper as [35]
The selected studies on people with diseases included European and Asian male and female participants.
“The selected studies included European [35, 40, 56, 64, 65, 98] and Asian [41, 99] male and female participants.”
From High-standard predictive equations for estimating body composition using bioelectrical impedance analysis: a systematic review (2024) · cites this paper as [35]
Seven equations for people with diseases were developed using single frequency devices at 50 kHz.
“Seven equations were developed using single frequency devices at 50 kHz [35, 40, 98], while 9 equations were made with multifrequency devices involving measures obtained at 50 [41, 64, 65, 99] and 100 kHz [56].”
From High-standard predictive equations for estimating body composition using bioelectrical impedance analysis: a systematic review (2024) · cites this paper as [35]
Cites
Tabachnick, B. G., & Fidell, L. S. (1983) · Cited by 79,888 · see citing works
(2007) · Cited by 10,092 · see citing works
Prevalence and Trends in Obesity Among US Adults, 1999-2008
Flegal, K. M. (2010) · JAMA · doi:10.1001/jama.2009.2014 · Cited by 7,580 · see citing works
Bioelectrical impedance analysis?part I: review of principles and methods
KYLE, U. (2004) · Clinical Nutrition · doi:10.1016/j.clnu.2004.06.004 · Cited by 3,200 · see citing works
Dual Energy X-Ray Absorptiometry Body Composition Reference Values from NHANES
Kelly, T. L., Wilson, K. E., & Heymsfield, S. B. (2009) · PLoS ONE · doi:10.1371/journal.pone.0007038 · Cited by 1,028 · see citing works
Huxley, R., Mendis, S., Zheleznyakov, E., Reddy, S., & Chan, J. C. N. (2009) · European Journal of Clinical Nutrition · doi:10.1038/ejcn.2009.68 · Cited by 772 · see citing works
Roche, A. F., Heymsfield, S. B., & Lohman, T. G. (2005) · Medical Entomology and Zoology · Cited by 743 · see citing works
New bioimpedance analysis system: improved phenotyping with whole-body analysis
Pietrobelli, A., Rubiano, F., St‐Onge, M., & Heymsfield, S. B. (2004) · European Journal of Clinical Nutrition · doi:10.1038/sj.ejcn.1601993 · Cited by 378 · see citing works
Relative expansion of extracellular fluid in obese vs. nonobese women
Waki, M., Kral, J. G., Mazariegos, M., Wang, J., Pierson, R. N., & Heymsfield, S. B. (1991) · American Journal of Physiology-Endocrinology and Metabolism · doi:10.1152/ajpendo.1991.261.2.e199 · Cited by 203 · see citing works
Limitations and validation of bioelectrical impedance analysis in morbidly obese patients
Coppini, L. Z., Waitzberg, D. L., & Campos, A. C. L. (2005) · Current Opinion in Clinical Nutrition & Metabolic Care · doi:10.1097/01.mco.0000165013.54696.64 · Cited by 199 · see citing works
Williams, J. E., Wells, J. C. K., Wilson, C. M., Haroun, D., Lucas, A., & Fewtrell, M. S. (2006) · American Journal of Clinical Nutrition · doi:10.1093/ajcn/83.5.1047 · Cited by 199 · see citing works
Comparison of Three Bioelectrical Impedance Methods with DXA in Overweight and Obese Men
Pateyjohns, I. R., Brinkworth, G. D., Buckley, J. D., Noakes, M., & Clifton, P. M. (2006) · Obesity · doi:10.1038/oby.2006.241 · Cited by 187 · see citing works
Das, S. K., Roberts, S. B., Kehayias, J. J., Wang, J., Hsu, L. K. G., Shikora, S. A., Saltzman, E., & McCrory, M. A. (2003) · American Journal of Physiology-Endocrinology and Metabolism · doi:10.1152/ajpendo.00185.2002 · Cited by 183 · see citing works
Bosy‐Westphal, A., Eichhorn, C., Kutzner, D., Illner, K., Heller, M., & Müller, M. J. (2003) · Journal of Nutrition · doi:10.1093/jn/133.7.2356 · Cited by 146 · see citing works
Bioelectrical impedance underestimates total and truncal fatness in abdominally obese women
Neovius, M., Hemmingsson, E., Freyschuss, B., & Uddén, J. (2006) · Obesity · doi:10.1038/oby.2006.199 · Cited by 119 · see citing works
iDXA, Prodigy, and DPXL Dual-Energy X-ray Absorptiometry Whole-Body Scans: A Cross-Calibration Study
Hull, H. R., He, Q. W., Thornton, J. C., Javed, F., Allen, L., Wang, J., Pierson, R. N., & Gallagher, D. (2008) · Journal of Clinical Densitometry · doi:10.1016/j.jocd.2008.09.004 · Cited by 113 · see citing works
Lazzer, S., Bedogni, G., Agosti, F., De Col, A., Mornati, D., & Sartorio, A. (2008) · British Journal of Nutrition · doi:10.1017/s0007114508922558 · Cited by 106 · see citing works
Body composition measurement in severe obesity
Das, S. K. (2005) · Current Opinion in Clinical Nutrition & Metabolic Care · doi:10.1097/01.mco.0000171122.60665.5f · Cited by 86 · see citing works
Nordstrand, N., Gjevestad, E. S., Dinh, K., Hofsø, D., Røislien, J., Saltvedt, E., Os, I., & Hjelmesaeth, J. S. (2011) · BMC Cardiovascular Disorders · doi:10.1186/1471-2261-11-7 · Cited by 81 · see citing works
Wells, J. C. K., Haroun, D., Williams, J. E., Wilson, C., Darch, T. S., Viner, R., Eaton, S. D., & Fewtrell, M. S. (2010) · International Journal of Obesity · doi:10.1038/ijo.2009.249 · Cited by 64 · see citing works
New body fat prediction equations for severely obese patients
Horie, L. M., González, M. C., Torrinhas, R. S., de Mello, M. T., Cecconello, I., & Waitzberg, D. L. (2008) · Clinical Nutrition · doi:10.1016/j.clnu.2008.03.011 · Cited by 51 · see citing works
Resting energy expenditure and fat-free mass do not decline during aging in severely obese women
Monda, M., Messina, G., Mangoni, C., & Luca, B. D. (2008) · Clinical Nutrition · doi:10.1016/j.clnu.2008.04.005 · Cited by 51 · see citing works
Traditional Anthropometric Parameters Still Predict Metabolic Disorders in Women With Severe Obesity
Ledoux, S., Coupaye, M., Essig, M., Msika, S., Roy, C., Quéguiner, I., Clérici, C., & Larger, É. (2009) · Obesity · doi:10.1038/oby.2009.349 · Cited by 48 · see citing works
Bioimpedance analysis: Should it be used in morbid obesity?
Leal, A. A. D., Faintuch, J., Morais, Á. A. C., Noe, J. A. B., Bertollo, D. M., Morais, R. C., & Cabrini, D. (2011) · American Journal of Human Biology · doi:10.1002/ajhb.21143 · Cited by 32 · see citing works
Sato, S., Demura, S., Kitabayashi, T., & Noguchi, T. (2007) · Journal of PHYSIOLOGICAL ANTHROPOLOGY · doi:10.2114/jpa2.26.533 · Cited by 29 · see citing works
Alvarez, V. P., Dixon, J. B., Strauss, B. J., Laurie, C., Chaston, T. B., & O’Brien, P. E. (2007) · Obesity Surgery · doi:10.1007/s11695-007-9032-3 · Cited by 24 · see citing works
Sun, S. S., & Chumlea, W. C. (2005) · doi:10.5040/9781492596950.ch-011 · Cited by 10 · see citing works
Flagged first, then by citations
Cited by
Sorted by citations
Gomez-Arbelaez, D., Bellido, D., Castro, A. I., Ordoñez-Mayan, L., Carreira, J., Galban, C., Martinez-Olmos, M. A., Crujeiras, A. B., Sajoux, I., & Casanueva, F. F. (2017) · The Journal of Clinical Endocrinology & Metabolism · doi:10.1210/jc.2016-2385 · Cited by 214 · see citing works
Self-citationFuture lines of research on phase angle: Strengths and limitations
Bellido, D., García, C. G., Talluri, A., Lukaski, H. C., & García‐Almeida, J. M. (2023) · Reviews in Endocrine and Metabolic Disorders · doi:10.1007/s11154-023-09803-7 · Cited by 214 · see citing works
“), skeletal muscle assessment in healthy subjects and individuals with altered muscle mass such as sarcopenia or cancer [25, 26].”
BackgroundSelf-citationSchollenberger, A. E., Karschin, J., Meile, T., Küper, M. A., Königsrainer, A., & Bischoff, S. C. (2016) · Nutrition · doi:10.1016/j.nut.2015.08.005 · Cited by 118 · see citing works
Segmental bioelectrical impedance analysis
Ward, L. C. (2012) · Current Opinion in Clinical Nutrition & Metabolic Care · doi:10.1097/mco.0b013e328356b944 · Cited by 114 · see citing works
“The methods by which this is achieved are largely unknown as the algorithms used are proprietary to the manufacturers [18]; however, it is likely that they are based on a notional ‘weight of the segment’ derived from the predicted segment FFM and percentage of fat mass derived from regression formulae determined in in…”
MethodCampa, F., Coratella, G., Cerullo, G., Noriega, Z., Francisco, R., Charrier, D., Irurtia, A., Lukaski, H., Silva, A. M., & Paoli, A. (2024) · Journal of Translational Medicine · doi:10.1186/s12967-024-05272-x · Cited by 80 · see citing works
“Risk of bias was high in two [34, 35] and unclear in six [36–41] studies due to the low to moderate (R 2 80”
BackgroundMethodInfluentialBallesteros-Pomar, M. D., Arnáiz, E. G., de la Maza, B. P., Galindo, D. E. B., Cobo, D. A., Roza, L. G., & Cano‐Rodríguez, I. (2021) · Nutrition · doi:10.1016/j.nut.2021.111442 · Cited by 78 · see citing works
How to Estimate Fat Mass in Overweight and Obese Subjects
Donini, L. M., Poggiogalle, E., del Balzo, V., Lubrano, C., Faliva, M. A., Opizzi, A., Perna, S., Pinto, A., & Rondanelli, M. (2013) · International Journal of Endocrinology · doi:10.1155/2013/285680 · Cited by 54 · see citing works
Friedrich, A. E., Damms-Machado, A., Meile, T., Scheuing, N., Stingel, K., Basrai, M., Küper, M. A., Kramer, K. M., Königsrainer, A., & Bischoff, S. C. (2013) · Obesity Surgery · doi:10.1007/s11695-013-1036-6 · Cited by 51 · see citing works
“Moreover, BIA has recently been indicated to provide comparable results to dual-energy X-ray absorptiometry or nuclear magnetic resonance [24, 25] and it is the only technique which allows calculation of the phase angle, which has been shown to be a marker for malnutrition [26, 27].”
Willig, A. L., Wright, L. Y., & Galvin, T. A. (2018) · Journal of the Academy of Nutrition and Dietetics · doi:10.1016/j.jand.2017.12.007 · Cited by 42 · see citing works
Vilallonga, R., Pereira-Cunill, J. L., Morales-Conde, S., Alarcón, I., Bretón, I., Domínguez-Adame, E., Ferrer, J. V., Ruiz-de-Gordejuela, A. G., Goday, A., Lecube, A., et al. (2019) · Obesity Surgery · doi:10.1007/s11695-019-04043-8 · Cited by 26 · see citing works
Bedogni, G., Agosti, F., Col, A. D., Marazzi, N., Tagliaferri, A., & Sartório, A. (2013) · European Journal of Clinical Nutrition · doi:10.1038/ejcn.2013.159 · Cited by 34 · see citing works
Achamrah, N., Jésus, P., Grigioni, S., Rimbert, A., Petit, A., Déchelotte, P., Folope, V., & Coëffier, M. (2018) · Nutrients · doi:10.3390/nu10010063 · Cited by 30 · see citing works
“BIA equations developed in obese subjects should be used to enhance the accuracy of the REE equations in this population [33,34].”
MethodCannon, T., & Choi, J. (2019) · Sensors · doi:10.3390/s19224825 · Cited by 25 · see citing works
“Whole-body BIA has struggled with the morbidly obese, which was addressed by one study that generated new prediction equations for segmental BIA [22].”
Tuorkey, M. J. (2012) · Journal of Biosensors & Bioelectronics · doi:10.4172/2155-6210.1000121 · Cited by 22 · see citing works
“[73] morbidly obese 159 DXA FFM Data input RJL BIA parameters provide accurate estimates of body composition in MO subjects”
Preliminary evaluation of the prevalence of sarcopenia in obese patients from Southern Italy
Rosa, E. D., Santarpia, L., Marra, M., Sammarco, R., Amato, V., Onufrio, M., SIMONE, G. D., Contaldo, F., & Pasanisi, F. (2014) · Nutrition · doi:10.1016/j.nut.2014.04.025 · Cited by 21 · see citing works
“It exposes the patient tominimal radiation but its equipment is not portable and use of DXA in morbidly obese patients (in particular with BMI >40 kg/m2 and weight >120 kg) is limited [29].”
BackgroundResultLéger‐Guist'hau, J., Domingues-Faria, C., Miolanne, M., Peyrol, F., Gerbaud, L., Perreira, B., Minet‐Quinard, R., Sapin, V., Walrand, S., & Boirie, Y. (2016) · Journal of Human Nutrition and Dietetics · doi:10.1111/jhn.12405 · Cited by 20 · see citing works
Body composition assessment in individuals with class II/III obesity: a narrative review
Silveira, É. A., Castro, M. C. R., Rezende, A. T. O., Rodrigues, A. P. D. S., Delpino, F. M., Oliveira, E. S., Corgosinho, F. C., & de Oliveira, C. M. (2024) · BMC Nutrition · doi:10.1186/s40795-024-00913-2 · Cited by 18 · see citing works
Modified body adiposity index for body fat estimation in severe obesity
Bernhard, A. B., Scabim, V. M., Serafim, M. P., Gadducci, A. V., Santo, M. A., & de Cleva, R. (2016) · Journal of Human Nutrition and Dietetics · doi:10.1111/jhn.12404 · Cited by 18 · see citing works
Fat-Free Mass, Metabolically Healthy Obesity, and Type 2 Diabetes in Severely Obese Asian Adults
Pramyothin, P., Limpattanachart, V., Dawilai, S., Sarasak, R., Sukaruttanawong, C., Chaiyasoot, K., Keawtanom, S., & Yamwong, P. (2017) · Endocrine Practice · doi:10.4158/ep171792.or · Cited by 15 · see citing works
Kulovitz, M. G., Kolkmeyer, D., Conn, C. A., Cohen, D. A., & Ferraro, R. T. (2014) · Nutrition · doi:10.1016/j.nut.2013.06.008 · Cited by 15 · see citing works
BODY MASS INDEX IN STUDENTS: THE PRESENT STATE OF THE PROBLEM
Peshkov, M. V., & Sharaykina, E. P. (2014) · Siberian medical review · doi:10.20333/25000136-2014-4-49-56 · Cited by 15 · see citing works
Bedogni, G., Grugni, G., Tringali, G., Agosti, F., & Sartório, A. (2014) · Annals of Human Biology · doi:10.3109/03014460.2014.990922 · Cited by 15 · see citing works
“Although it is well accepted that obese non-PWS subjects need population-specific equations (Bedogni et al., 2013; Das et al., 2003; Jiménez et al., 2012; Sartorio et al., 2005), the hypothesis that obese PWS subjects may require population-specific equations because of their lower FFM has remained untested for the la…”
BackgroundKılınç, H., Günay, M., Kaplan, Ş., & Bayrakdar, A. (2018) · Journal of Human Sciences · doi:10.14687/jhs.v15i3.5299 · Cited by 15 · see citing works
Depression Severity as a Risk Factor of Sarcopenic Obesity in Morbidly Obese Patients
Venant, V., Pouget, M., Lahaye, C., Gentes, E., Pereira, B., Lambert, C., Debarges, J., Domingues-Faria, C., Palmier-Forestier, C., Farigon, N., et al. (2019) · The journal of nutrition health & aging · doi:10.1007/s12603-019-1218-6 · Cited by 12 · see citing works
Pedrera‐Zamorano, J. D., Roncero‐Martín, R., García, J. M. L., García, J. F. C., Rey‐Sánchez, P., Vera, V., Martínez, M., & Morán, J. M. (2014) · American Journal of Human Biology · doi:10.1002/ajhb.22669 · Cited by 11 · see citing works
Body Composition Evaluation in Severe Obesity: A Critical Review
de Cleva, R. (2016) · Advances in Obesity Weight Management & Control · doi:10.15406/aowmc.2016.04.00113 · Cited by 11 · see citing works
“Jiménez & cols, [25] Barcelona, Spain Prospective and cross-sectional Morbid obesity, DXA, BIA, body composition 126 women and 33 men 45.”
BackgroundMethodInfluentialHazart, J., Montel, F., Gentes, E., Lahaye, C., Pouget, M., Farigon, N., Miolanne, M., Mulliez, A., & Boirie, Y. (2022) · Children · doi:10.3390/children10010027 · Cited by 8 · see citing works
Pouget, M., Pinel, A., Miolanne, M., Gentes, E., Picard, M., Martinez, R., Mulliez, A., Guillet, C., Farigon, N., & Boirie, Y. (2024) · Obesity · doi:10.1002/oby.24157 · Cited by 6 · see citing works
“Body composition was estimated using multifrequency bioimpe-dance analysis (Bodystat Quadscan 4000, Bodystat Ltd., Sulby, Isle of Man) according to the equation by Jiménez et al. [12].”
Flores, L., Janka, M., Canivell, S., Jiménez, A., & Vidal, J. (2013) · Diabetes Research and Clinical Practice · doi:10.1016/j.diabres.2013.05.008 · Cited by 5 · see citing works
Self-citationComment évaluer les besoins énergétiques et protéiques du sujet obèse ?
Jésus, P., & Coëffier, M. (2017) · Nutrition Clinique et Métabolisme · doi:10.1016/j.nupar.2017.09.008 · Cited by 4 · see citing works
Perea, V., Jiménez, A., Flores, L., Ortega, E., Coves, M. J., & Vidal, J. (2012) · Surgery for Obesity and Related Diseases · doi:10.1016/j.soard.2012.05.010 · Cited by 3 · see citing works
“However, we overcame this limitation by using BIA equations that had been developed by our group specifically for the MO population and validated using DXA as the reference method [9].”
MethodInfluentialSelf-citationCvetko, E. D., Drenjančević, I., Nešić, N., & Ahić, J. M. (2014) · Hrčak Portal of scientific journals of Croatia (University Computing Centre) · Cited by 3 · see citing works
Nugraha, R., Suherman, A., Ray, H. R. D., & Ma’mun, A. (2021) · Journal of Engineering Research · doi:10.36909/jer.asseee.16061 · Cited by 2 · see citing works
“After the measurement, Tanita BC-418MA provided data on the estimated body fat percentage (Jiménez et al., 2012).”
MethodADDITIONAL ABDOMINAL MEASUREMENTS ARE A USEFUL TOOL TO EVALUATE BODY COMPOSITION IN OBESE WOMEN
Triffoni‐Melo, A. D. T., Monte-Alegre, F. A., Leandro‐Merhi, V. A., & Garcia, R. W. D. (2019) · Arquivos de Gastroenterologia · doi:10.1590/s0004-2803.201900000-55 · Cited by 2 · see citing works
“Jiménez et al.(21) developed equations to estimate total and segmental body composition in severely obese Caucasians.”
BackgroundNugraha, R., Suherman, A., Ray, H. R. D., & Ma’mun, A. (2020) · International Conference on Sport Science, Health, and Physical Education (ICSSHPE) · doi:10.2991/ahsr.k.200214.051 · Cited by 1 · see citing works
“The development of technology nowadays there are already many instruments that can easily evaluate body composition and distribution of body fat [11,17].”
BackgroundBody Composition Changes and Reduction of Risk for Metabolic Syndrome After Bariatric Surgery
Baudino-Burgarello, E., Cohen, D. A., Cerami, J., & Conn, C. A. (2014) · Topics in Clinical Nutrition · doi:10.1097/01.tin.0000443024.21277.0f
Alencar, M. (2017) · Obesity & Control Therapies Open Access · doi:10.15226/2374-8354/4/1/00129
Geneen, L. J. (2014) · Queen Margaret University Publications Repository (Queen Margaret University)
Campa, F., Coratella, G., Cerullo, G., Noriega, Z., Francisco, R., Charrier, D., Irurtia, A., Lukaski, H. C., Silva, A. M., & Paoli, A. (2024) · Figshare · doi:10.6084/m9.figshare.26727262
- Authors
- Jiménez, A., Omaña, W., Flores, L., Coves, M. J., Bellido, D., Perea, V., & Vidal, J.
- Affiliations
- Hospital Clínic de Barcelona (ES)
- Centro de Investigación Biomédica en Red Diabetes y Enfermedades Metabólicas Asociadas (ES)
- Complejo Hospitalario Universitario de Ferrol (ES)
- Corresponding author
- Jiménez, Amanda
- Venue
- Obesity Surgery
- Published
- 2011-12-15
- Volume
- 22
- Issue
- 4
- Pages
- 587-593
- ISSN
- 0960-8923 (ISSN-L)
- 1708-0428
- OpenAlex
- W2050428815
- Cited by
- 40
- References
- 27
- Citations by year
- 2024: 4 · 2023: 1 · 2022: 1 · 2021: 2 · 2020: 1 · 2019: 4 · 2018: 3 · 2017: 3 · 2016: 4 · 2015: 2 · 2014: 7 · 2013: 5 · 2012: 3
- FWCI
- 3.27
- Type
- journal article
- Language
- en
- Open access
- green
- hdl.handle.net
- Licence
- other-oa
- www.springer.com/tdm (tdm)
- Locations
- Obesity Surgery (publishedVersion)
- PubMed (publishedVersion)
- RUNA. Repositorio Institucional del Sergas (Servicio Gallego de Salud (SERGAS)) (submittedVersion, other-oa)
- Topics
- Health Sciences › Medicine › Physiology › Body Composition Measurement Techniques
- Health Sciences › Medicine › Public Health, Environmental and Occupational Health › Obesity, Physical Activity, Diet
- Physical Sciences › Engineering › Electrical and Electronic Engineering › Electrical and Bioimpedance Tomography
- Keywords
- morbid obesity, body composition
- MeSH terms
- Adult, Algorithms, Body Composition, Cross-Sectional Studies, Female, Humans, Male, Obesity, Morbid, Predictive Value of Tests, Spain, Reproducibility of Results, Cohort Studies, Absorptiometry, Photon, Electric Impedance, Statistics, Nonparametric
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
