{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,31]],"date-time":"2026-10-31T01:38:55Z","timestamp":1793410735668,"version":"4.5.0"},"reference-count":62,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2025,7,1]],"date-time":"2025-07-01T00:00:00Z","timestamp":1751328000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0"},{"start":{"date-parts":[[2025,7,1]],"date-time":"2025-07-01T00:00:00Z","timestamp":1751328000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Sci Rep"],"DOI":"10.1038\/s41598-025-06019-4","type":"journal-article","created":{"date-parts":[[2025,7,1]],"date-time":"2025-07-01T17:15:30Z","timestamp":1751390130000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":11,"title":["Beyond accuracy: a framework for evaluating algorithmic bias and performance, applied to automated sleep scoring"],"prefix":"10.1038","volume":"15","author":[{"given":"Michal","family":"Bechny","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Luigi","family":"Fiorillo","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Julia","family":"van der Meer","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Markus","family":"Schmidt","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Claudio","family":"Bassetti","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Athina","family":"Tzovara","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Francesca","family":"Faraci","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2025,7,1]]},"reference":[{"key":"6019_CR1","doi-asserted-by":"crossref","unstructured":"Berry, R.\u00a0B. et al. AASM scoring manual version 2.2 updates: new chapters for scoring infant sleep staging and home sleep apnea testing (2015).","DOI":"10.5664\/jcsm.5176"},{"key":"6019_CR2","doi-asserted-by":"publisher","first-page":"406","DOI":"10.1001\/archinte.164.4.406","volume":"164","author":"S Redline","year":"2004","unstructured":"Redline, S. et al. The effects of age, sex, ethnicity, and sleep-disordered breathing on sleep architecture. Arch. Intern. Med. 164, 406\u2013418 (2004).","journal-title":"Arch. Intern. Med."},{"key":"6019_CR3","doi-asserted-by":"publisher","first-page":"1025","DOI":"10.1016\/j.sleep.2008.12.008","volume":"10","author":"C Sahlin","year":"2009","unstructured":"Sahlin, C., Franklin, K. A., Stenlund, H. & Lindberg, E. Sleep in women: normal values for sleep stages and position and the effect of age, obesity, sleep apnea, smoking, alcohol and hypertension. Sleep Med. 10, 1025\u20131030 (2009).","journal-title":"Sleep Med."},{"key":"6019_CR4","doi-asserted-by":"publisher","first-page":"74","DOI":"10.1111\/j.1365-2869.2008.00700.x","volume":"18","author":"H Danker-Hopfe","year":"2009","unstructured":"Danker-Hopfe, H. et al. Interrater reliability for sleep scoring according to the rechtschaffen & kales and the new aasm standard. J. Sleep Res. 18, 74\u201384 (2009).","journal-title":"J. Sleep Res."},{"key":"6019_CR5","doi-asserted-by":"publisher","first-page":"81","DOI":"10.5664\/jcsm.2350","volume":"9","author":"RS Rosenberg","year":"2013","unstructured":"Rosenberg, R. S. & Van Hout, S. The american academy of sleep medicine inter-scorer reliability program: sleep stage scoring. J. Clin. Sleep Med. 9, 81\u201387 (2013).","journal-title":"J. Clin. Sleep Med."},{"key":"6019_CR6","doi-asserted-by":"publisher","first-page":"193","DOI":"10.5664\/jcsm.9538","volume":"18","author":"YJ Lee","year":"2022","unstructured":"Lee, Y. J., Lee, J. Y., Cho, J. H. & Choi, J. H. Interrater reliability of sleep stage scoring: a meta-analysis. J. Clin. Sleep Med. 18, 193\u2013202 (2022).","journal-title":"J. Clin. Sleep Med."},{"key":"6019_CR7","doi-asserted-by":"publisher","first-page":"e13956","DOI":"10.1111\/jsr.13956","volume":"33","author":"S Nikkonen","year":"2024","unstructured":"Nikkonen, S. et al. Multicentre sleep-stage scoring agreement in the sleep revolution project. J. Sleep Res. 33, e13956 (2024).","journal-title":"J. Sleep Res."},{"key":"6019_CR8","first-page":"145","volume":"26","author":"N Natarajan","year":"2013","unstructured":"Natarajan, N., Dhillon, I. S., Ravikumar, P. K. & Tewari, A. Learning with noisy labels. Adv. Neural Inf. Process. Syst. 26, 145 (2013).","journal-title":"Adv. Neural Inf. Process. Syst."},{"key":"6019_CR9","doi-asserted-by":"publisher","first-page":"8135","DOI":"10.1109\/TNNLS.2022.3152527","volume":"34","author":"H Song","year":"2022","unstructured":"Song, H., Kim, M., Park, D., Shin, Y. & Lee, J.-G. Learning from noisy labels with deep neural networks: a survey. IEEE Trans. Neural Netw. Learn. Syst. 34, 8135\u20138153 (2022).","journal-title":"IEEE Trans. Neural Netw. Learn. Syst."},{"key":"6019_CR10","doi-asserted-by":"publisher","first-page":"845","DOI":"10.1109\/TNNLS.2013.2292894","volume":"25","author":"B Fr\u00e9nay","year":"2013","unstructured":"Fr\u00e9nay, B. & Verleysen, M. Classification in the presence of label noise: a survey. IEEE Trans. Neural Netw. Learn. Syst. 25, 845\u2013869 (2013).","journal-title":"IEEE Trans. Neural Netw. Learn. Syst."},{"key":"6019_CR11","doi-asserted-by":"publisher","first-page":"72","DOI":"10.1038\/s41746-021-00440-5","volume":"4","author":"M Perslev","year":"2021","unstructured":"Perslev, M. et al. U-sleep: resilient high-frequency sleep staging. NPJ Digital Med. 4, 72 (2021).","journal-title":"NPJ Digital Med."},{"key":"6019_CR12","doi-asserted-by":"publisher","first-page":"33","DOI":"10.1038\/s41746-023-00784-0","volume":"6","author":"L Fiorillo","year":"2023","unstructured":"Fiorillo, L. et al. U-sleep\u2019s resilience to aasm guidelines. NPJ Digital Med. 6, 33 (2023).","journal-title":"NPJ Digital Med."},{"key":"6019_CR13","doi-asserted-by":"publisher","first-page":"e70092","DOI":"10.7554\/eLife.70092","volume":"10","author":"R Vallat","year":"2021","unstructured":"Vallat, R. & Walker, M. P. An open-source, high-performance tool for automated sleep staging. Elife 10, e70092 (2021).","journal-title":"Elife"},{"key":"6019_CR14","doi-asserted-by":"publisher","first-page":"1955","DOI":"10.1109\/TNSRE.2020.3011181","volume":"28","author":"A Guillot","year":"2020","unstructured":"Guillot, A., Sauvet, F., During, E. H. & Thorey, V. Dreem open datasets: Multi-scored sleep datasets to compare human and automated sleep staging. IEEE Trans. Neural Syst. Rehabil. Eng. 28, 1955\u20131965 (2020).","journal-title":"IEEE Trans. Neural Syst. Rehabil. Eng."},{"key":"6019_CR15","doi-asserted-by":"crossref","unstructured":"Gordon, M.\u00a0L., Zhou, K., Patel, K., Hashimoto, T. & Bernstein, M.\u00a0S. The disagreement deconvolution: Bringing machine learning performance metrics in line with reality. In Proceedings of the 2021 CHI Conference on Human Factors in Computing Systems 1\u201314 (2021).","DOI":"10.1145\/3411764.3445423"},{"key":"6019_CR16","doi-asserted-by":"publisher","first-page":"555","DOI":"10.2147\/NSS.S455649","volume":"2024","author":"M Bechny","year":"2024","unstructured":"Bechny, M. et al. Bridging ai and clinical practice: integrating automated sleep scoring algorithm with uncertainty-guided physician review. Nat. Sci. Sleep 2024, 555\u2013572 (2024).","journal-title":"Nat. Sci. Sleep"},{"key":"6019_CR17","doi-asserted-by":"crossref","unstructured":"Gerke, S., Minssen, T. & Cohen, G. Ethical and legal challenges of artificial intelligence-driven healthcare. In Artificial Intelligence in Healthcare 295\u2013336 (Elsevier, 2020).","DOI":"10.1016\/B978-0-12-818438-7.00012-5"},{"key":"6019_CR18","doi-asserted-by":"publisher","first-page":"44","DOI":"10.1038\/s41591-018-0300-7","volume":"25","author":"EJ Topol","year":"2019","unstructured":"Topol, E. J. High-performance medicine: the convergence of human and artificial intelligence. Nat. Med. 25, 44\u201356 (2019).","journal-title":"Nat. Med."},{"key":"6019_CR19","doi-asserted-by":"publisher","first-page":"2199","DOI":"10.1001\/jama.2018.17163","volume":"320","author":"EH Shortliffe","year":"2018","unstructured":"Shortliffe, E. H. & Sep\u00falveda, M. J. Clinical decision support in the era of artificial intelligence. JAMA 320, 2199\u20132200 (2018).","journal-title":"JAMA"},{"key":"6019_CR20","doi-asserted-by":"publisher","first-page":"121","DOI":"10.5664\/jcsm.26814","volume":"3","author":"MH Silber","year":"2007","unstructured":"Silber, M. H. et al. The visual scoring of sleep in adults. J. Clin. Sleep Med. 3, 121\u2013131 (2007).","journal-title":"J. Clin. Sleep Med."},{"key":"6019_CR21","doi-asserted-by":"publisher","first-page":"885","DOI":"10.5664\/jcsm.5894","volume":"12","author":"M Younes","year":"2016","unstructured":"Younes, M., Raneri, J. & Hanly, P. Staging sleep in polysomnograms: analysis of inter-scorer variability. J. Clin. Sleep Med. 12, 885\u2013894 (2016).","journal-title":"J. Clin. Sleep Med."},{"key":"6019_CR22","doi-asserted-by":"crossref","unstructured":"Bechny, M. et al. Framework for algorithmic bias quantification and its application to automated sleep scoring. In 2024 11th IEEE Swiss Conference on Data Science (SDS)  250\u2013253 (IEEE, 2024).","DOI":"10.1109\/SDS60720.2024.00045"},{"key":"6019_CR23","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1145\/3457607","volume":"54","author":"N Mehrabi","year":"2021","unstructured":"Mehrabi, N., Morstatter, F., Saxena, N., Lerman, K. & Galstyan, A. A survey on bias and fairness in machine learning. ACM computing surveys (CSUR) 54, 1\u201335 (2021).","journal-title":"ACM computing surveys (CSUR)"},{"key":"6019_CR24","doi-asserted-by":"publisher","first-page":"170","DOI":"10.1038\/s41746-023-00913-9","volume":"6","author":"MD Abr\u00e0moff","year":"2023","unstructured":"Abr\u00e0moff, M. D. et al. Considerations for addressing bias in artificial intelligence for health equity. NPJ digital medicine 6, 170 (2023).","journal-title":"NPJ digital medicine"},{"key":"6019_CR25","doi-asserted-by":"publisher","first-page":"20230023","DOI":"10.1259\/bjr.20230023","volume":"96","author":"JW Gichoya","year":"2023","unstructured":"Gichoya, J. W. et al. Ai pitfalls and what not to do: mitigating bias in ai. Br. J. Radiol. 96, 20230023 (2023).","journal-title":"Br. J. Radiol."},{"key":"6019_CR26","unstructured":"Bland, J. Statistical methods for assessing agreement between two methods of clinical measurement. Lancet (1986)."},{"key":"6019_CR27","doi-asserted-by":"publisher","first-page":"143","DOI":"10.1111\/j.1440-1681.2009.05288.x","volume":"37","author":"J Ludbrook","year":"2010","unstructured":"Ludbrook, J. Confidence in altman-bland plots: a critical review of the method of differences. Clin. Exp. Pharmacol. Physiol. 37, 143\u2013149 (2010).","journal-title":"Clin. Exp. Pharmacol. Physiol."},{"key":"6019_CR28","doi-asserted-by":"crossref","unstructured":"Nagendran, M. et al. Artificial intelligence versus clinicians: systematic review of design, reporting standards, and claims of deep learning studies. bmj368 (2020).","DOI":"10.1136\/bmj.m689"},{"key":"6019_CR29","unstructured":"R Core Team. R: A Language and Environment for Statistical Computing. R Foundation for Statistical Computing, Vienna, Austria (2025)."},{"key":"6019_CR30","doi-asserted-by":"publisher","first-page":"e1820","DOI":"10.1212\/WNL.0000000000008844","volume":"94","author":"R Budhiraja","year":"2020","unstructured":"Budhiraja, R. et al. Prevalence and correlates of periodic limb movements in osa and the effect of cpap therapy. Neurology 94, e1820\u2013e1827 (2020).","journal-title":"Neurology"},{"key":"6019_CR31","doi-asserted-by":"publisher","first-page":"70","DOI":"10.1016\/j.smrv.2016.07.002","volume":"34","author":"CV Senaratna","year":"2017","unstructured":"Senaratna, C. V. et al. Prevalence of obstructive sleep apnea in the general population: a systematic review. Sleep Med. Rev. 34, 70\u201381 (2017).","journal-title":"Sleep Med. Rev."},{"key":"6019_CR32","unstructured":"Eilers, P.\u00a0H., Marx, B.\u00a0D. & Durb\u00e1n, M. Twenty years of p-splines. SORT: Stat. Oper. Res. Trans. 39, 0149\u2013186 (2015)."},{"key":"6019_CR33","doi-asserted-by":"publisher","first-page":"165","DOI":"10.1046\/j.1365-2869.2001.00252.x","volume":"10","author":"H Gaudreau","year":"2001","unstructured":"Gaudreau, H., Carrier, J. & Montplaisir, J. Age-related modifications of nrem sleep eeg: from childhood to middle age. J. Sleep Res. 10, 165\u2013172 (2001).","journal-title":"J. Sleep Res."},{"key":"6019_CR34","doi-asserted-by":"publisher","first-page":"401","DOI":"10.1016\/j.sleep.2013.11.791","volume":"15","author":"W Moraes","year":"2014","unstructured":"Moraes, W. et al. Effects of aging on sleep structure throughout adulthood: a population-based study. Sleep Med. 15, 401\u2013409 (2014).","journal-title":"Sleep Med."},{"key":"6019_CR35","doi-asserted-by":"publisher","first-page":"901","DOI":"10.1093\/sleep\/23.7.1e","volume":"23","author":"RG Norman","year":"2000","unstructured":"Norman, R. G., Pal, I., Stewart, C., Walsleben, J. A. & Rapoport, D. M. Interobserver agreement among sleep scorers from different centers in a large dataset. Sleep 23, 901\u2013908 (2000).","journal-title":"Sleep"},{"key":"6019_CR36","doi-asserted-by":"publisher","first-page":"191","DOI":"10.1007\/s11325-014-0990-0","volume":"19","author":"X Zhang","year":"2015","unstructured":"Zhang, X. et al. Process and outcome for international reliability in sleep scoring. Sleep Breath. 19, 191\u2013195 (2015).","journal-title":"Sleep Breath."},{"key":"6019_CR37","doi-asserted-by":"publisher","first-page":"63","DOI":"10.1046\/j.1365-2869.2003.00375.x","volume":"13","author":"H Danker-Hopfe","year":"2004","unstructured":"Danker-Hopfe, H. et al. Interrater reliability between scorers from eight european sleep laboratories in subjects with different sleep disorders. J. Sleep Res. 13, 63\u201369 (2004).","journal-title":"J. Sleep Res."},{"key":"6019_CR38","doi-asserted-by":"publisher","first-page":"1255","DOI":"10.1093\/sleep\/27.7.1255","volume":"27","author":"MM Ohayon","year":"2004","unstructured":"Ohayon, M. M., Carskadon, M. A., Guilleminault, C. & Vitiello, M. V. Meta-analysis of quantitative sleep parameters from childhood to old age in healthy individuals: developing normative sleep values across the human lifespan. Sleep 27, 1255\u20131273 (2004).","journal-title":"Sleep"},{"key":"6019_CR39","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1016\/j.cger.2007.08.007","volume":"24","author":"JRD Espiritu","year":"2008","unstructured":"Espiritu, J. R. D. Aging-related sleep changes. Clin. Geriatr. Med. 24, 1\u201314 (2008).","journal-title":"Clin. Geriatr. Med."},{"key":"6019_CR40","doi-asserted-by":"publisher","first-page":"19","DOI":"10.1016\/j.neuron.2017.02.004","volume":"94","author":"BA Mander","year":"2017","unstructured":"Mander, B. A., Winer, J. R. & Walker, M. P. Sleep and human aging. Neuron 94, 19\u201336 (2017).","journal-title":"Neuron"},{"key":"6019_CR41","doi-asserted-by":"publisher","first-page":"722","DOI":"10.1164\/ajrccm.149.3.8118642","volume":"149","author":"S Redline","year":"1994","unstructured":"Redline, S., Kump, K., Tishler, P. V., Browner, I. & Ferrette, V. Gender differences in sleep disordered breathing in a community-based sample. Am. J. Respir. Crit. Care Med. 149, 722\u2013726 (1994).","journal-title":"Am. J. Respir. Crit. Care Med."},{"key":"6019_CR42","doi-asserted-by":"publisher","first-page":"464","DOI":"10.1002\/ana.24593","volume":"79","author":"J Haba-Rubio","year":"2016","unstructured":"Haba-Rubio, J. et al. Prevalence and determinants of periodic limb movements in the general population. Ann. Neurol. 79, 464\u2013474 (2016).","journal-title":"Ann. Neurol."},{"key":"6019_CR43","doi-asserted-by":"publisher","first-page":"84","DOI":"10.1016\/j.slsci.2016.06.002","volume":"9","author":"V Mancebo-Sosa","year":"2016","unstructured":"Mancebo-Sosa, V. et al. Sleep architecture alterations in patients with periodic limb movements disorder during sleep and sleep breathing disorders. Sleep Sci. 9, 84\u201388 (2016).","journal-title":"Sleep Sci."},{"key":"6019_CR44","doi-asserted-by":"publisher","first-page":"207","DOI":"10.1016\/j.ijcard.2013.08.088","volume":"169","author":"X Wang","year":"2013","unstructured":"Wang, X. et al. Obstructive sleep apnea and risk of cardiovascular disease and all-cause mortality: a meta-analysis of prospective cohort studies. Int. J. Cardiol. 169, 207\u2013214 (2013).","journal-title":"Int. J. Cardiol."},{"key":"6019_CR45","doi-asserted-by":"publisher","first-page":"161","DOI":"10.1037\/met0000301","volume":"26","author":"M Papenberg","year":"2021","unstructured":"Papenberg, M. & Klau, G. W. Using anticlustering to partition data sets into equivalent parts. Psychol. Methods 26, 161 (2021).","journal-title":"Psychol. Methods"},{"key":"6019_CR46","doi-asserted-by":"crossref","unstructured":"Norori, N., Hu, Q., Aellen, F.\u00a0M., Faraci, F.\u00a0D. & Tzovara, A. Addressing bias in big data and ai for health care: A call for open science. Patterns 2 (2021).","DOI":"10.1016\/j.patter.2021.100347"},{"key":"6019_CR47","doi-asserted-by":"crossref","unstructured":"Bin Rafiq, R., Modave, F., Guha, S. & Albert, M.\u00a0V. Validation methods to promote real-world applicability of machine learning in medicine. In Proceedings of the 2020 3rd International Conference on Digital Medicine and Image Processing  13\u201319 (2020).","DOI":"10.1145\/3441369.3441372"},{"key":"6019_CR48","doi-asserted-by":"crossref","unstructured":"Stasinopoulos, M.\u00a0D., Rigby, R.\u00a0A., Heller, G.\u00a0Z., Voudouris, V. & De Bastiani, F. Flexible regression and smoothing: using GAMLSS in R (CRC Press, 2017).","DOI":"10.1201\/b21973"},{"key":"6019_CR49","doi-asserted-by":"crossref","unstructured":"Rigby, R.\u00a0A., Stasinopoulos, M.\u00a0D., Heller, G.\u00a0Z. & De Bastiani, F. Distributions for modeling location, scale, and shape: Using GAMLSS in R (Chapman and Hall\/CRC, 2019).","DOI":"10.1201\/9780429298547"},{"key":"6019_CR50","doi-asserted-by":"publisher","first-page":"6476","DOI":"10.21037\/jtd-21-1353","volume":"13","author":"P Drakatos","year":"2021","unstructured":"Drakatos, P. et al. Periodic limb movements during sleep: a narrative review. J. Thorac. Dis. 13, 6476 (2021).","journal-title":"J. Thorac. Dis."},{"key":"6019_CR51","doi-asserted-by":"publisher","first-page":"5229","DOI":"10.1038\/s41467-018-07229-3","volume":"9","author":"JB Stephansen","year":"2018","unstructured":"Stephansen, J. B. et al. Neural network analysis of sleep stages enables efficient diagnosis of narcolepsy. Nat. Commun. 9, 5229 (2018).","journal-title":"Nat. Commun."},{"key":"6019_CR52","doi-asserted-by":"crossref","unstructured":"Bakker, J.\u00a0P. et al. Scoring sleep with artificial intelligence enables quantification of sleep stage ambiguity: hypnodensity based on multiple expert scorers and auto-scoring. Sleep46, zsac154 (2023).","DOI":"10.1093\/sleep\/zsac154"},{"key":"6019_CR53","doi-asserted-by":"crossref","unstructured":"Lohia, P.\u00a0K. et al. Bias mitigation post-processing for individual and group fairness. In Icassp 2019-2019 ieee international conference on acoustics, speech and signal processing (icassp)  2847\u20132851 (IEEE, 2019).","DOI":"10.1109\/ICASSP.2019.8682620"},{"key":"6019_CR54","doi-asserted-by":"crossref","unstructured":"Kim, M.\u00a0P., Ghorbani, A. & Zou, J. Multiaccuracy: Black-box post-processing for fairness in classification. In Proceedings of the 2019 AAAI\/ACM Conference on AI, Ethics, and Society  247\u2013254 (2019).","DOI":"10.1145\/3306618.3314287"},{"key":"6019_CR55","unstructured":"Bird, S. et al. Fairlearn: a toolkit for assessing and improving fairness in ai. In Microsoft, Tech. Rep. MSR-TR-2020-32 (2020)."},{"key":"6019_CR56","unstructured":"Hufthammer, K.\u00a0T., Aasheim, T.\u00a0H., \u00c5nneland, S., Brynjulfsen, H. & Slavkovik, M. Bias mitigation with aif360: a comparative study. In NIKT: Norsk IKT-konferanse for forskning og utdanning 2020 (Norsk IKT-konferanse for forskning og utdanning, 2020)."},{"key":"6019_CR57","doi-asserted-by":"publisher","first-page":"457","DOI":"10.1007\/s10994-021-05946-3","volume":"110","author":"E H\u00fcllermeier","year":"2021","unstructured":"H\u00fcllermeier, E. & Waegeman, W. Aleatoric and epistemic uncertainty in machine learning: an introduction to concepts and methods. Mach. Learn. 110, 457\u2013506 (2021).","journal-title":"Mach. Learn."},{"key":"6019_CR58","doi-asserted-by":"crossref","unstructured":"Nguyen, A., Yosinski, J. & Clune, J. Deep neural networks are easily fooled: high confidence predictions for unrecognizable images. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition  427\u2013436 (2015).","DOI":"10.1109\/CVPR.2015.7298640"},{"key":"6019_CR59","doi-asserted-by":"publisher","first-page":"20210068","DOI":"10.1098\/rspa.2021.0068","volume":"478","author":"A Goyal","year":"2022","unstructured":"Goyal, A. & Bengio, Y. Inductive biases for deep learning of higher-level cognition. Proc. R. Soc. A 478, 20210068 (2022).","journal-title":"Proc. R. Soc. A"},{"key":"6019_CR60","doi-asserted-by":"publisher","first-page":"1061","DOI":"10.1037\/0033-295X.111.4.1061","volume":"111","author":"D Borsboom","year":"2004","unstructured":"Borsboom, D., Mellenbergh, G. J. & Van Heerden, J. The concept of validity. Psychol. Rev. 111, 1061 (2004).","journal-title":"Psychol. Rev."},{"key":"6019_CR61","doi-asserted-by":"crossref","unstructured":"Casella, G. & Berger, R. Statistical inference (CRC press, 2024).","DOI":"10.1201\/9781003456285"},{"key":"6019_CR62","unstructured":"Kales, A., Rechtschaffen, A., University of California, L. A. B. I.\u00a0S. & (U.S.), N. N. I.\u00a0N. A Manual of Standardized Terminology, Techniques and Scoring System for Sleep Stages of Human Subjects: Allan Rechtschaffen and Anthony Kales, Editors. NIH publication (U. S. National Institute of Neurological Diseases and Blindness, Neurological Information Network, 1968)."}],"container-title":["Scientific Reports"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.nature.com\/articles\/s41598-025-06019-4.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/www.nature.com\/articles\/s41598-025-06019-4","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/www.nature.com\/articles\/s41598-025-06019-4.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,7,1]],"date-time":"2025-07-01T18:05:22Z","timestamp":1751393122000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.nature.com\/articles\/s41598-025-06019-4"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,7,1]]},"references-count":62,"journal-issue":{"issue":"1","published-online":{"date-parts":[[2025,12]]}},"alternative-id":["6019"],"URL":"https:\/\/doi.org\/10.1038\/s41598-025-06019-4","relation":{},"ISSN":["2045-2322"],"issn-type":[{"value":"2045-2322","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,7,1]]},"assertion":[{"value":"14 April 2025","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"5 June 2025","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"1 July 2025","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"The authors report no conflict of interest in the study.","order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing interests"}},{"value":"The secondary usage of the Berner Sleep-Wake Registry (BSWR) dataset was approved by the local ethics committee (Kantonale Ethikkommission Bern [KEK]-Nr. 2022-00415), ensuring compliance with the Human Research Act (HRA) and Ordinance on Human Research with the Exception of Clinical Trials (HRO). All methods were carried out in accordance with relevant guidelines and regulations. Written informed consent was obtained from all participants, as part of the general consent process introduced at Inselspital in 2015. Data were maintained with confidentiality throughout the study.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethics approval"}}],"article-number":"21421"}}