The DOI matches its Crossref record; 111 of the 112 references it cites were found in a registry or an index.
Earlier versus later cognitive event-related potentials (ERPs) in attention-deficit/hyperactivity disorder (ADHD): A meta-analysis
Open accessAbstract
The current meta-analysis summarizes relevant literature on earlier (P100, N100, P200, N200, ERN/Ne) versus later (P300, Pe, CNV) cognitive Event-Related Potential (ERP) differences between children, adolescents, and adults with Attention-Deficit/Hyperactivity Disorder (ADHD) and without ADHD (non-ADHD). Furthermore, the heterogeneity in previous research is addressed by analyzing potentially relevant demographic and methodological moderators (age group, IQ, medication, comorbidity, task, cognitive function, modality, inter-stimulus-interval, number of electrodes). Via database search 52 relevant articles were identified including n = 1576 ADHD and n = 1794 non-ADHD. Using multilevel-models, pooled effect sizes were calculated. For earlier components, individuals with ADHD showed shorter Go-P100-latencies than non-ADHD. For later ERPs, individuals with ADHD showed smaller Cue-P300-amplitudes, longer Go-P300-latencies, smaller NoGo-P300-amplitudes, longer NoGo-P300-latencies, smaller CNV-amplitudes, and smaller Pe-amplitudes. The substantial heterogeneity identified for most of the ERP components could be explained by the demographic and methodological moderators of interest. This meta-analysis identified relevant moderate group differences (-0.32<d<-0.57), mainly regarding later cognitive ERPs. Nevertheless, results are characterized by substantial heterogeneity and the moderate effect sizes (d<0.6) limit the use for clinical application.
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
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?
The source backs part of the claim, not all of it — or backs it for a different population, condition or comparison.
In the citing paper
“ADHD and OCD are common psychiatric disorders that may be differentiated by neurocognitive measures and neural indices of error monitoring (Figuracion et al. 2024; Hanna et al. 2026; Kaiser et al. 2020; Kofler et al. 2013; Lamothe et al. 2025; Liu et al. 2020; Lutz et al. 2021; Tamm et al. 2012).”
From Reaction Time Variability and Electrophysiological Indices of Performance Monitoring in Youths With Attention-Deficit/Hyperactivity Disorder and Obsessive-Compulsive Disorder (2026) · cites this paper as Kaiser et al. 2020
Experimental — accuracy not yet measuredThe cited source says what the claim attributes to it.
In the citing paper
“However, one meta‐analysis noted a blunted Pe was associated more consistently with ADHD than a blunted ERN (Kaiser et al. 2020).”
From Reaction Time Variability and Electrophysiological Indices of Performance Monitoring in Youths With Attention-Deficit/Hyperactivity Disorder and Obsessive-Compulsive Disorder (2026) · cites this paper as Kaiser et al. 2020
The cited source says what the claim attributes to it.
In the citing paper
“Our Pe results are consistent with a meta‐analysis that concluded a reduced Pe is associated more reliably with ADHD than a reduced ERN (Kaiser et al. 2020) and with the recent finding of a blunted Pe in a large study of adolescents with ADHD (Figuracion et al. 2024).”
From Reaction Time Variability and Electrophysiological Indices of Performance Monitoring in Youths With Attention-Deficit/Hyperactivity Disorder and Obsessive-Compulsive Disorder (2026) · cites this paper as Kaiser et al. 2020
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cites
R: A Language and Environment for Statistical Computing
R Core Team (2000) · doi:10.32614/r.manuals · Cited by 355,893
Preferred reporting items for systematic reviews and meta-analyses: the PRISMA Statement.
(2009) · PubMed Central · Cited by 44,859
Preferred reporting items for systematic reviews and meta-analyses: The PRISMA statement
Moher, D., Liberati, A., Tetzlaff, J., Altman, D. G., & the PRISMA Group* (2009) · Annals of Internal Medicine · doi:10.7326/0003-4819-151-4-200908180-00135 · Cited by 37,554
Conducting Meta-Analyses in R with the metafor Package
Viechtbauer, W. (2010) · Journal of Statistical Software · doi:10.18637/jss.v036.i03 · Cited by 20,464
Wasserman, S. S., Hedges, L. V., & Olkin, I. (1988) · Journal of Educational Statistics · doi:10.2307/1164953 · Cited by 10,377
Updating P300: An integrative theory of P3a and P3b
Polich, J. (2007) · Clinical Neurophysiology · doi:10.1016/j.clinph.2007.04.019 · Cited by 7,919
A basic introduction to fixed-effect and random-effects models for meta-analysis
Borenstein, M., Hedges, L. V., Higgins, J. P. T., & Rothstein, H. R. (2010) · Research Synthesis Methods · doi:10.1002/jrsm.12 · Cited by 6,793
The worldwide prevalence of ADHD: a systematic review and metaregression analysis
Polanczyk, G., de Lima, M. S., Horta, B. L., Biederman, J., & Rohde, L. A. (2007) · American Journal of Psychiatry · doi:10.1176/ajp.2007.164.6.942 · Cited by 5,346
Statistical Methods for Meta-Analysis.
Freeman, P. R., Hedges, L. V., & Olkin, I. (1986) · Biometrics · doi:10.2307/2531069 · Cited by 4,840
Polanczyk, G. V., Salum, G. A., Sugaya, L. S., Caye, A., & Rohde, L. A. (2015) · Journal of Child Psychology and Psychiatry · doi:10.1111/jcpp.12381 · Cited by 3,990
Flagged first, then by citations
1 of the 112 references couldn’t be matched to a known paper.
Cited by
Sorted by citations
Items 101–169 of 169
Papp, S., Tombor, L., Kakuszi, B., Réthelyi, J., Bitter, I., & Czobor, P. (2023) · Journal of Neural Transmission · doi:10.1007/s00702-023-02639-0 · Cited by 3
P100 in major depressive disorder and healthy controls: An emotion-processing task
Kratochwil, Z., Kesik, J., Gökçelli, D. K., Scherbaum, N., Frodl, T., & Müller, B. W. (2025) · International Journal of Psychophysiology · doi:10.1016/j.ijpsycho.2025.113281 · Cited by 2
Härpfer, K., & Riesel, A. (2025) · Clinical Psychology Review · doi:10.1016/j.cpr.2025.102673 · Cited by 2
Zhou, Q., & Luo, Y. (2022) · Research in Developmental Disabilities · doi:10.1016/j.ridd.2022.104262 · Cited by 2
Guo, X., Yu, J., Quan, C., Xiao, J., Wang, J., Zhang, B., Hao, X., Wu, X., & Liang, J. (2024) · Journal of Psychiatric Research · doi:10.1016/j.jpsychires.2024.02.004 · Cited by 2
Digital Interventions for Attention Deficit/Hyperactivity Disorder
Marten, L., Kaiser, A., & Häge, A. (2024) · Integrated science · doi:10.1007/978-3-031-76414-1_11 · Cited by 2
Self-citationThe Utility of Biomarkers for Assessment and Intervention in Neurodevelopmental Disorders
Guldner, S., Ernst, J., Nees, F., & Holz, N. E. (2024) · Integrated science · doi:10.1007/978-3-031-76414-1_3 · Cited by 2
Self-citationTime to inhibit: P300 amplitude differences in individuals with high and low temporal efficiency
Krystecka, K., Stanczyk, M., Choiński, M., Szela̧g, E., & Szymaszek, A. (2025) · Cerebral Cortex · doi:10.1093/cercor/bhae500 · Cited by 2
Brewer, L. M., Patel, J. R., Andrasik, F., Sable, J. J., Williams, M. T., Vorhees, C. V., & Sable, H. J. K. (2025) · Genes · doi:10.3390/genes16060672 · Cited by 2
Mendes, A. J., Lema, A., Carvalho, S., & Leite, J. (2024) · PeerJ · doi:10.7717/peerj.17144 · Cited by 2
Hyperkinetic disorder and attention deficiency and hyperactivity syndrome in adults
Gorodnicheva, A. V., Ponomarev, I. A., & Sidenkova, A. P. (2023) · Ural Medical Journal · doi:10.52420/2071-5943-2023-22-3-108-117 · Cited by 1
Facial Affect Recognition and Executive Function Abnormalities in ADHD Subjects: An ERP Study
Vosough, S., Candrian, G., Kasper, J., Rehim, H. A., Eich, D., Müller, A. F., & Jäncke, L. (2024) · Clinical EEG and Neuroscience · doi:10.1177/15500594241304492 · Cited by 4
Biabani, M., Walsh, K. S., Zhou, S.-H., Wagner, J., Johnstone, A., Paterson, J., Johnson, B. P., Matthews, N. L., Loughnane, G. M., O’Connell, R. G., et al. (2025) · Journal of Neuroscience · doi:10.1523/jneurosci.0469-24.2025 · Cited by 3
Chueh, T., Tsai, Y.-J., Wu, J., Chu, C.-L., Wu, C.-T., & Hung, T. (2025) · Research in Developmental Disabilities · doi:10.1016/j.ridd.2025.104993 · Cited by 1
Horakova, G., Weibel, S., Tissot, U., Pham, B., Costache, M. E., & Bonnefond, A. (2026) · Cortex · doi:10.1016/j.cortex.2026.07.005 · Cited by 1
Wu, W., Huang, Y., Liu, X., Li, Z., Zheng, W., Wang, W., Kang, C., & Li, Y. (2025) · Clinical EEG and Neuroscience · doi:10.1177/15500594251382654 · Cited by 1
Neurophysiological alterations during sensory processing in autism - a meta-analysis
Ghosh, A., Nasarre-Nacenta, N., Baumeister, S., Holz, N. E., Banaschewski, T., Brandeis, D., Aggensteiner, P.-M., & Kaiser, A. (2025) · European Child & Adolescent Psychiatry · doi:10.1007/s00787-025-02917-0 · Cited by 3
Self-citationLow- and high-tech AAC approaches in severe acquired brain injury: an exploratory pilot study
Luca, R. D., Gangemi, A., Fabio, R. A., Rifici, C., Sergi, G., Cuffaro, D., Piccione, M., Bonanno, M., Maggio, M. G., Cardile, D., et al. (2026) · Disability and Rehabilitation Assistive Technology · doi:10.1080/17483107.2026.2632735 · Cited by 1
Gao, X., Zhong, L., He, H., Zhang, J., & Yang, W. (2026) · Brain and Development · doi:10.1016/j.braindev.2026.104517 · Cited by 1
Asché, L., Pakos, J., Schrage, H., Schuder, J., Jung, L., Sanchez, D., Selaskowski, B., Wiebe, A., Philipsen, A., & Braun, N. (2026) · JMIR Human Factors · doi:10.2196/85351 · Cited by 1
Toward precision EEG: assessing the reliability of individual-level ERPs across EEG systems
Korisky, A., Rabinovitch, E., Yahav, P. H.-S., McCandliss, B. D., & Golumbic, E. Z. (2026) · Frontiers in Human Neuroscience · doi:10.3389/fnhum.2026.1763477 · Cited by 2
Kopańska, M., Trojniak, J., & Pąchalska, M. (2026) · Frontiers in Neuroscience · doi:10.3389/fnins.2026.1834391 · Cited by 1
Oliveros, J. C., & Kreither, J. (2021) · Archivos de Neurociencias · doi:10.31157/archneurosciencesmex.v26i01.210 · Cited by 1
Innovations in Neurophysiology and Its Role in Neuropsychiatry
Öz, D., & Yener, G. (2022) · Nöro Psikiyatri Arşivi · doi:10.29399/npa.28234 · Cited by 1
Prasad, R., Tarai, S., & Bit, A. (2024) · Computer Methods in Biomechanics & Biomedical Engineering · doi:10.1080/10255842.2024.2311876 · Cited by 1
The Effect of Rhythmic Audio-Visual Stimulation on Inhibitory Control: An ERP Study
Wang, Y., Wu, D., Sun, K., Zhu, Y., Chen, X., & Xiao, W. (2024) · Brain Sciences · doi:10.3390/brainsci14050506 · Cited by 2
Neurophysiological Insights into ADHD: Electroencephalography Methods in Diagnosis and Treatment
Yazıcı, M. S., & Serdengeçti, N. (2024) · Springer eBooks · doi:10.1007/978-3-031-32035-4_150-1 · Cited by 1
Neural Markers of Anticipated Task Difficulty: An EEG Study With Auditory Similarity Judgments
Song, Z., Higashi, H., & Ishii, S. (2025) · IEEE Transactions on Neural Systems and Rehabilitation Engineering · doi:10.1109/tnsre.2025.3574709 · Cited by 1
Attention Deficit Hyperactivity Disorder: Advances in Epidemiology, Pathogenesis, and Neurobiology
Bautista-Vanegas, F. E., Rosales, R. R. G., Cabezas-Soliz, I. N., Vallejos-Rejas, D. R. E., Rocha, C. J. S., Auza-Santiváñez, J. C., Castedo, L. R., Espejo-Alanoca, D., & Santander, I. O. (2025) · Neurodivergences · doi:10.56294/neuro2025189 · Cited by 1
SUZUKI, K. (2023) · Japanese Journal of Physiological Psychology and Psychophysiology · doi:10.5674/jjppp.2311si · Cited by 1
Berchio, C., Annen, L. C., Bouamoud, Y., & Micali, N. (2022) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2022.03.24.485666 · Cited by 1
Levy, O., Hackmon, S. L., Zvilichovsky, Y., Korisky, A., Bidet‐Caulet, A., Schweitzer, J. B., & Golumbic, E. Z. (2024) · eLife · doi:10.7554/elife.103235.1 · Cited by 1
Sub-Second Fluctuation between Top-Down and Bottom-Up Modes Distinguishes Diverse Human Brain StatesPreprint
Park, Y., Cha, Y.-H., Kim, H., Kim, Y., Woo, J. H., Cho, H., Mashour, G. A., Xu, T., Lee, U., Hong, S., et al. (2025) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2025.03.12.642768 · Cited by 3
Hanna, G. L., Liu, Y., Hanna, B. S., Arnold, P. D., & Gehring, W. J. (2026) · Psychophysiology · doi:10.1111/psyp.70403
“ADHD and OCD are common psychiatric disorders that may be differentiated by neurocognitive measures and neural indices of error monitoring (Figuracion et al. 2024; Hanna et al. 2026; Kaiser et al. 2020; Kofler et al. 2013; Lamothe et al. 2025; Liu et al. 2020; Lutz et al. 2021; Tamm et al. 2012).”
InfluentialBerchio, C., Annen, L. C., Bouamoud, Y., & Micali, N. (2022) · SSRN Electronic Journal · doi:10.2139/ssrn.4072588
Bellato, A., Norman, L. J., Idrees, I., Ogawa, C. Y., Waitt, A. E., Zuccolo, P. F., Tye, C., Raduà, J., Groom, M. J., & Shephard, E. (2021) · doi:10.31219/osf.io/t2kus
Shephard, E., Groom, M. J., & Jackson, G. M. (2022) · International review of movement disorders · doi:10.1016/bs.irmvd.2021.11.002
Eggermont, J. J. (2023) · Elsevier eBooks · doi:10.1016/b978-0-443-15548-2.00008-9
Eidelman-Rothman, M., Reuveni, O., Keil, A., Kritzman, L., Freche, D., Okon‐Singer, H., & Levit‐Binnun, N. (2023) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2023.10.21.563393
Training langsamer kortikaler Potenziale
Ströhle, G., Hirsch, S., & Sidiropoulos, K. (2023) · Springer eBooks · doi:10.1007/978-3-662-65726-3_16
Efstratopoulou, M., & AlQahtani, O. (2023) · Advances in psychology, mental health, and behavioral studies (APMHBS) book series · doi:10.4018/978-1-6684-6040-5.ch009
Non-pharmacological Interventions for Mental Disorders
(2024) · Frontiers research topics · doi:10.3389/978-2-8325-4708-3
Gupta, R. S., Simmons, A. N., Dugas, N. N., Stout, D. M., & Harlé, K. M. (2024) · Frontiers in Human Neuroscience · doi:10.3389/fnhum.2024.1357868
Iravani, E., Faramarzi, S., & jamaloei, H. R. (2025) · doi:10.61838/kman.jayps.6.1.1
Levy, O., Hackmon, S. L., Zvilichovsky, Y., Korisky, A., Bidet‐Caulet, A., Schweitzer, J. B., & Golumbic, E. Z. (2025) · eLife · doi:10.7554/elife.103235.2
Aktivierung und Aufmerksamkeitslenkung
Lauth, G. W., & Lauth-Lebens, M. (2025) · Springer eBooks · doi:10.1007/978-3-662-70064-8_4
Neurophysiological Alterations during Sensory Processing in Autism - A Meta-AnalysisPreprint
Ghosh, A., Nasarre-Nacenta, N., Baumeister, S., Holz, N. E., Banaschewski, T., Brandeis, D., Aggensteiner, P.-M., & Kaiser, A. (2025) · medRxiv · doi:10.1101/2025.05.15.25327383
Self-citationNeurophysiological Insights into ADHD: Electroencephalography Methods in Diagnosis and Treatment
Yazıcı, M. S., & Serdengeçti, N. (2025) · Springer eBooks · doi:10.1007/978-3-031-73368-0_150
Li, X., Zhang, Z., Xiao, L., Zhang, X., Yao, H., Li, F., Chen, R., & Zhong, Q. (2025) · Frontiers in Human Neuroscience · doi:10.3389/fnhum.2025.1626528
EEG-based time-on-task vigilance trajectories in children with and without ADHD
Candrian, G., Pershin, I., Rostami, M., Ackeret, M., & Müller, A. F. (2025) · International Journal of Psychophysiology · doi:10.1016/j.ijpsycho.2025.113264
The effects of transcranial random noise stimulation on excitation/inhibition balance in ADHD
Dakwar-Kawar, O., Francis, A. J. A., Arya, R., Mairon, N., Mishra, J., Berger, I., Kadosh, R. C., Balasubramani, P. P., & Nahum, M. (2025) · NeuroImage Clinical · doi:10.1016/j.nicl.2025.103923
Diamanti, G., Colin, J., Hasler, R., Ros, T., Perroud, N., & Deiber, M. (2025) · Clinical Neurophysiology Practice · doi:10.1016/j.cnp.2025.11.005 · Cited by 1
Jensen, T. J., Muir, A. M., Gleave, E., Christensen, B. A., Corbin, E. M., Allen, W., Clawson, A., Farrer, T. J., Jaggi, T., Alder, N. J., et al. (2026) · Neuropsychologia · doi:10.1016/j.neuropsychologia.2026.109358
Aydın, Ü., Wang, Z., Gyurkovics, M., Tong, A., Cullen, G., Ahmed, S., Palmer, J., & McLoughlin, G. (2026) · Translational Psychiatry · doi:10.1038/s41398-026-03938-2 · Cited by 1
A study on cortical habituation based on event-related potential P50 and CNV
Li, J., Zhang, Y., Li, H., Liu, C., Sun, Z. X., Fu, Y., He, Q., & Lv, Y. (2026) · Frontiers in Neurology · doi:10.3389/fneur.2026.1787063
Lynch, J. D., Subedi, S., Zimon, M., Peisch, V. D., & Arnett, A. B. (2026) · Journal of Attention Disorders · doi:10.1177/10870547261438181
Panda, E. J., Arulnandhy, S., Tekok-Kilic, A., Segalowitz, S., & Marshall, W. J. (2026) · Developmental Cognitive Neuroscience · doi:10.1016/j.dcn.2026.101732
Pranjić, M., Peisch, V. D., Li, Y., Troxel, M., Lee, K., & Arnett, A. B. (2026) · Journal of Attention Disorders · doi:10.1177/10870547261445680 · Cited by 1
Nawarathna, G. S., Ariyasinghe, D. I., Balasooriya, N., Fernando, A., & Dassanayake, T. L. (2026) · Nutritional Neuroscience · doi:10.1080/1028415x.2026.2659148
Bogomolova, E. V., Mikhailova, A. A., Semenova, P. A., & Nagornev, S. N. (2026) · Fizioterapevt (Physiotherapist) · doi:10.33920/med-14-2602-10
Soto, E. F., Shuter, M., Shea, N., Schiros, A., Nord, J., Kuo, Y. C., Sun, H., Antshel, K. M., & Russo, N. N. (2026) · Journal of Autism and Developmental Disorders · doi:10.1007/s10803-026-07357-6
Zhang, Y., Li, T., Jiang, J., Li, H., Sun, R., Li, Y., & Chen, W. (2026) · NeuroImage Clinical · doi:10.1016/j.nicl.2026.104011
Jaeger, C. (2026) · Springer eBooks · doi:10.1007/978-3-032-20450-9_6
Boer, O. D., Lutz, M. C., Marroun, H. E., Hajcak, G., & Franken, I. H. A. (2026) · Psychophysiology · doi:10.1111/psyp.70330
A bibliometric analysis of executive functions in ADHD mapping trends and hotspots
Zhou, Q., Chen, C., Tong, J., & Han, P. (2026) · Discover Mental Health · doi:10.1007/s44192-026-00495-4
Sub-second fluctuations between top-down and bottom-up modes distinguish diverse human brain states
Park, Y., Cha, Y.-H., Kim, H., Kim, Y., Woo, J. H., Lee, J., Cho, H., Mashour, G. A., Xu, T., Lee, U., et al. (2026) · Current Biology · doi:10.1016/j.cub.2026.06.014 · Cited by 1
Atypical auditory cortical processing in ADHD during early and middle childhood
Pranjić, M., Likitlersuang, J., & Arnett, A. B. (2026) · Developmental Cognitive Neuroscience · doi:10.1016/j.dcn.2026.101780
Rasool, I., Bashir, D. J., Mehmood, R., Ahmad, M., Faheem, M. M., Malik, A., & Bhat, K. A. (2026) · International review of neurobiology · doi:10.1016/bs.irn.2026.08.025
Quinn, D., Earley, C., Scarlat, I.-M., Whelan, R., & McGrath, J. (2026) · Journal of Attention Disorders · doi:10.1177/10870547261483457
Metadata
- Authors
- Kaiser, A., Aggensteiner, P.-M., Baumeister, S., Holz, N. E., Banaschewski, T., & Brandeis, D.
- Affiliations
- Heidelberg University (DE)
- University of Zurich (CH)
- Corresponding author
- Kaiser, Anna
- Venue
- Neuroscience & Biobehavioral Reviews
- Publisher
- Elsevier BV
- Published
- 2020-05
- Volume
- 112
- Pages
- 117-134
- ISSN
- 0149-7634 (ISSN-L)
- OpenAlex
- W3002747956
- Cited by
- 171
- References
- 112
- Citations by year
- 2026: 24 · 2025: 37 · 2024: 29 · 2023: 27 · 2022: 21 · 2021: 28 · 2020: 5
- FWCI
- 3.36
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- Locations
- Neuroscience & Biobehavioral Reviews (publishedVersion, cc-by-nc-nd)
- PubMed (publishedVersion)
- Zurich Open Repository and Archive (University of Zurich) (acceptedVersion, cc-by-nc-nd)
- Repository for Publications and Research Data (ETH Zurich) (submittedVersion, cc-by-nc-nd)
- Zurich Open Repository and Archive (University of Zurich) (submittedVersion, cc-by-nc-nd)
- ETH Zürich Research Collection
- Zurich Open Repository and Archive (University of Zurich)
- Funders
- Bundesministerium für Bildung und Forschung (FKZ 01EE1408E)
- Topics
- Health Sciences › Medicine › Psychiatry and Mental health › Attention Deficit Hyperactivity Disorder
- Life Sciences › Neuroscience › Cognitive Neuroscience › Neural and Behavioral Psychology Studies
- Social Sciences › Psychology › Neuropsychology and Physiological Psychology › Developmental and Educational Neuropsychology
- Keywords
- attention deficit hyperactivity disorder, event-related potentials, P300, P100, N2 component, P200, contingent negative variation, P300 latency, P300 amplitude, error-related negativity, ERP components, children and adolescents
- MeSH terms
- Adolescent, Adult, Attention Deficit Disorder with Hyperactivity, Child, Cognition, Evoked Potentials, Humans, Psychomotor Performance, Executive Function
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
