Abdominal Ultrasound Performance Assessment: A Comparison of Generic and Extended OSCE Rating Scales

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Extended, task-specific rating scales may offer detailed guidance, while more generic scales offer broader applicability and ease of use. This study compares whether and how ratings differed when using an extended versus a generic rating scale for assessing performance in basic abdominal ultrasound skills. Methods In this single-centre cohort study, 80 medical students participated in an OSCE for abdominal ultrasound rated by both an extended and generic rating scale in parallel. Five domains were evaluated: image settings, transducer handling, examination technique, image explanation and overall performance. Each OSCE station was rated by two assessors simultaneously, one using each scale. Results The generic rating scale demonstrated significantly higher internal consistency (mean Cronbach’s α generic 0.803 vs. extended 0.699; p=0.011) and a higher generalisability (Phi generic 0.529 vs. extended 0.466). Absolute ratings on the generic rating scale were significantly lower (performance P generic 75.38% vs. extended 77.99%, p<0.001), though in-between stations difficulty was only significantly different for two stations. Conclusion In total, the generic rating scale showed a more stringent rating with a higher internal consistency and a higher variance due to participants’ performance. Therefore, a well-designed generic rating scale can reliably and efficiently assess basic abdominal ultrasound performance, possibly offering greater generalisability and simpler implementation than extended task specific rating scales. Ultrasound education clinical evaluation assessment techniques rating scales near-peer teaching near-peer assessment Figures Figure 1 Background Objective structured clinical examinations (OSCE) are a common examination format for ultrasound courses as it captures its manifold facets best [ 1 – 5 ]. Their organisation, administration, and execution demand substantial knowledge, experience, and planning [ 6 , 7 ]. The creation of the OSCE rating scale is a key component of this process [ 7 ]. OSCE assessment requires rating scales to capture as much information as possible within limited observation time. While generic rating scales have shown advantages for less technical skills [ 8 – 10 ], it remains debated whether this also applies to technical skills such as ultrasound [ 11 , 12 ]. Accordingly, recent literature on ultrasound exams generally distinguishes into two options: task-specific (i.e. extended) rating scales [ 3 , 13 ] and generic rating scales [ 1 , 5 , 14 ]. Both types of rating scales, generic and extended, seem to provide advantages [ 15 ]. Supporters of extended rating scales argue they offer greater reliability and validity [ 3 , 13 , 16 ]. In contrast, supporters of a generic rating scale contend that the use of elaborate and procedure-specific tools may not always provide a better estimate of performance than scales relying on general competencies [ 5 , 15 , 17 , 18 ]. Furthermore, generic rating scales are easier to develop and can be quickly adapted to new OSCE stations, thereby simplifying OSCE organisation and coordination. Despite those facts, there has never been a head-to-head comparison of a generic and a task-specific extended rating scale for abdominal ultrasound; they have all been proposed and validated individually. In this study, we aimed to compare a generic and an extended rating scale evaluating the same domains. We investigated whether and how ratings differed in terms of internal consistency, generalisability and performance scores when using an extended versus a generic rating scale for assessing performance in basic abdominal ultrasound skills. Methods Context The Young Sonographers, a subsection of the Swiss Society for Ultrasound in Medicine (SGUM/SSUM), have provided courses on basic abdominal ultrasound since 2019, comprising five hours of e-learning and 16 hours of practical training taught by near-peer tutors [ 19 , 20 ]. Following a revision of this course on abdominal ultrasound [ 21 ], existing OSCE stations and their task-specific extended rating scales [ 13 ] were updated accordingly (unpublished thesis, available on request). Based on this revision, two rating scales were compared: one extended, task-specific version and one generic scale using the same domains. Study design This single-centre cohort study was conducted at the University of Bern to compare both rating scales in the abdominal ultrasound OSCE. Eighty medical students (3rd − 6th year; 65% female; mean age 23 years) who had completed the course and consented to participation were included. The study aimed to examine how both rating scales (extended, task-specific vs. generic) performed in terms of internal consistency and generalisability. Performance scores P (expressed as percentages out of a maximum of 30 points) were analysed across the rating scales, series and stations to better understand the sources of variability. OSCE organisation Twelve OSCE stations each assessed one course’s learning objective through a 4-minute practical ultrasound task. Stations were combined into eight six-station series (A1–D2; Appendix 1). Participants alternated between examiner and model roles. Each series was completed by two groups (n = 11/12) of participants, except for Series D1 and D2, which were completed by only one group each (n = 6). Rating scales Both rating scales used identical domains - image settings, transducer handling, examination technique, image interpretation, and overall performance - with a total of 30 points. The extended scale contained task-specific descriptors (Appendix 2), whereas the generic scale provided brief, uniform prompts (Fig. 1). Data collection Each OSCE station was rated by two assessors simultaneously, one using each scale. Assessors were randomly assigned and included two SGUM-accredited physicians and 23 trained near-peer tutors with ≥ 1 year of teaching experience. Separate information sessions were held for each group, which conveyed basics for the OSCE itself and the study format, as well as instructions on how to use the rating scales, followed by two exemplary training videos to ensure a common frame of reference [22]. For each video existed an optimal rating score proposed by experienced ultrasound assessors, which was used to lay ground for the discussion among the raters. Statistical Analysis To evaluate reliability, internal consistency was quantified using Cronbach's α, as a number between 0 and 1 [23]. We considered a Cronbach’s α of 0.7 and greater suitable for summative examinations such as ours [3, 12]. Generalisability Theory (GT) was used to assess the influence of non-rating scale-related factors on the final score (i.e. how much of variance was explained by the participants performance). Phi coefficients were calculated, ranging from 0 to 1, with 1 meaning that 100% of the variance in scores is due to the respondent's performance [24]. Phi coefficients were calculated for the following model: “(examinee : series) x station”. A Phi coefficient of 0.5 or higher was deemed adequate for this OSCE. A multivariate split-plot ANOVA was used to examine how rating scale (within-subjects), series and OSCE station (between-subjects) contributed to variability in performance scores (α ≤ 0.05). Effect sizes were reported using partial η 2 , interpreted as small ( 0.14). The level of significance was adapted using a post-hoc Bonferroni correction. If relevant differences between rating scales were found, paired-sample t-tests were performed to identify responsible OSCE stations. All statistical analyses were performed using SPSS for Windows, version 28 (IBM, Armonk, NY, USA). For the Generalizability Theory (GT), G_String, version 2019 was used. Ethical considerations The ethics committee of Bern, Switzerland declared this study was exempt from the Human Research Act and the need for a comprehensive ethical appraisal (BASEC number Req-2023-00254). Collected data was treated after being anonymised. All participants were informed about the study, then provided informed consent to participate or could withdraw without consequences on them receiving the course certificate. This study was conducted in accordance with the ethical principles outlined in the Declaration of Helsinki. Results Internal consistency and generalisability of rating scales Our results indicate that internal consistency was higher for the generic scale than for the extended rating scale (Cronbach’s α 0.803 vs 0.699; p = 0.011, Table 1 ). Across stations, Cronbach’s α ranged from 0.50–0.83 for the extended and 0.74–0.87 for the generic scale (Appendix 4). The generalisability analysis (GT) revealed a Phi coefficient of 0.466 for the extended and 0.529 for the generic scale (Table 1 ), suggesting that generic scales show greater performance-related variance than extended scales. The main results for both rating scales are indicated in Table 1 . Influence of rating scale on examinees’ performance In multivariate testing, using a split-plot ANOVA we found a significant interaction between type of rating scale and performance P (F(1, 461) = 22.95, p < 0.001, η² = 0.047; small effect), in other words the two rating scales rated participants’ performance significantly different with the generic rating scale attributing lower scores than the extended scale. Additionally, “series” and “station” showed significant effects (F(7, 461) = 2.22, p = 0.032, η² = 0.033; F(11, 461) = 3.95, p < 0.001, η² = 0.086; small–medium effects) on participants’ performance P. Though, a significant interaction between type of rating scale and station was found (F(11, 461) = 2.782, p = 0.002, η 2 = 0.062, small effect size), the interaction between type of rating scales and series was not significant (p = 0.108), suggesting the difference between scales was balanced across the 12 series. Finally, we tested individual OSCE stations between rating scales to identify whether all stations are responsible for the significant overall difference using paired samples t-tests. Analyses yielded statistically significant differences between participants’ performance P only for the stations “retroperitoneum” and “hepatic veins” (p < 0.001, Appendix 3). Discussion In this single-centre cohort study, we evaluated whether a generic and an extended rating scale for abdominal ultrasound skills yield comparable ratings on examinees’ performance in OSCE. We found that a generic rating scale rated participants’ performance with a higher internal consistency and generalisability, while attributing lower rating scores overall. Our findings are aligned with broader medical education literature questioning the superiority of task-specific rating scales in performance-based exams. Early on, Regehr et al. showed that global rating scales demonstrate higher reliability and validity than checklists [ 10 ], mirroring our observation that the generic scale produced more consistent ratings. Studies comparing generic domain-based versus extended checklist-based OSCE assessments likewise report that generic ratings better discriminate between levels of competence [ 8 , 9 ]. Although some authors argue that generic ratings scales may be less appropriate for technical skills [ 11 , 12 ], our results show that generic rating scales can also yield reliable assessments in a highly technical domain such as ultrasound. Within ultrasound education, our results complement the ongoing debate on the usefulness of detailed, task-specific rating scales. Hofer et al. advocated for extended scales to capture procedural complexity [ 3 ]. However, in our study, extended scales produced inflated scores particularly in anatomically complex stations (hepatic veins, retroperitoneum), suggesting that extensive rating scales may inadvertently mask performance differences. More recent work supports moving away from highly granular rating scales: Ma et al. identified critical point-of-care ultrasound rating items but recommended restricting assessment to essential behaviours [ 15 ], and Tolsgaard et al. emphasised evaluation of core domains, such as image optimisation, systematic examination, and interpretation, rather than long lists of procedural steps [ 17 ]. Our direct comparison provides empirical support for this shift, indicating that generic scales may better capture ultrasound performance by focusing on broader domains that reflect the integrated nature of the skill. Taken together, our findings suggest that generic rating scales may be a suitable choice when designing OSCE assessments for basic abdominal ultrasound. They may simplify the development of new stations and reduce examiner training burden by avoiding the creation of detailed, station-specific rating scales. Further research should explore whether these findings extend to less technical areas of medical education and whether faculty and near-peer assessors perform differently when using generic scales. Strength and limitations This study is the first to directly compare two rating scale formats for abdominal ultrasound OSCE assessment. To minimise cross-contamination of rating styles between assessors, we separated examiners into different break rooms and rotated them regularly. Our use of a diverse examiner group, including faculty and trained near-peer tutors, reflects real-world practice and supports generalisability for technical skills examination such as ultrasound. However, because only a small proportion of examiners were faculty, subgroup analysis was not feasible; results might differ in an assessor cohort composed entirely of faculty. Transferability to other OSCE contexts may also be limited by the narrow and well-defined task of abdominal ultrasound, where assessors share a relatively uniform mental model of optimal performance; conditions that may not hold in broader clinical domains. Conclusion For abdominal ultrasound OSCEs, generic rating scales appear to offer more stringent rating behaviour, higher internal consistency, and greater performance-related variance than extended scales. A well-designed generic rating scale can reliably and efficiently assess basic abdominal ultrasound competence, potentially offering greater generalisability and simpler implementation than extended, task-specific rating scales. Abbreviations OSCE Objective structured clinical examinations SGUM/SSUM Swiss society for ultrasound in medicine ANOVA Analysis of variance GT Generalizability theory Declarations Ethical considerations and consent to participation The ethics committee of Bern, Switzerland declared this study was exempt from the Human Research Act and the need for a comprehensive ethical appraisal (BASEC number Req-2023-00254). Collected data was treated after being anonymised. All participants were informed about the study, then provided informed consent to participate or could withdraw without consequences on them receiving the course certificate. This study was conducted in accordance with the ethical principles outlined in the Declaration of Helsinki. Conflicts of Interest The authors declare that they have no competing interests. Authors’ contributions SS, CB, DS and RW contributed to the conception of the work. RW, SS, CB and DS contributed to the design of the work. SS and RW did the acquisition, and DS performed the primary analysis. SS, DS, CB and RW interpreted the data. SS wrote the main manuscript and CB, DS, JB and RW substantively revised it. All authors have read and approved the final manuscript. Consent to publication All authors agreed to the publication. Manuscript funding This work did not receive external funding. Availability of data and materials The used data are available from the corresponding author upon request. Acknowledgements We would like to thank all the students, near-peer tutors and faculty tutors who participated in this study. References Bailitz J, O’Brien J, McCauley M, et al. Development of an expert consensus checklist for emergency ultrasound. AEM Educ Train. 2022;6:e10783. https://doi.org/10.1002/aet2.10783 . Bell C, Hall AK, Wagner N, et al. The Ultrasound Competency Assessment Tool (UCAT): Development and Evaluation of a Novel Competency-based Assessment Tool for Point‐of‐care Ultrasound. AEM Educ Train. 2021;5:e10520. https://doi.org/10.1002/aet2.10520 . Hofer M, Kamper L, Sadlo M, et al. Evaluation of an OSCE Assessment Tool for Abdominal Ultrasound Courses. Ultraschall Med - Eur J Ultrasound. 2011;32:184–90. https://doi.org/10.1055/s-0029-1246049 . Norcini J, Anderson B, Bollela V et al. Criteria for good assessment: Consensus statement and recommendations from the Ottawa 2010 Conference. Med Teach. 2011;33:206–14. https://doi.org/10.3109/0142159X.2011.551559 Teichgräber U, Ingwersen M, Ehlers C, et al. Integration of ultrasonography training into undergraduate medical education: catch up with professional needs. Insights Imaging. 2022;13:150. https://doi.org/10.1186/s13244-022-01296-3 . Khan KZ, Gaunt K, Ramachandran S, Organisation, et al. Adm Med Teach. 2013;35:e1447–63. https://doi.org/10.3109/0142159X.2013.818635 . Melcher P, Zajonz D, Roth A et al. Peer-assisted teaching student tutors as examiners in an orthopedic surgery OSCE station – pros and cons. GMS Interdiscip Plast Reconstr Surg DGPW 5Doc17 2016. https://doi.org/10.3205/IPRS000096 Giemsa P, Wübbolding C, Fischer MR, et al. What works best in a general practice specific OSCE for medical students: Mini-CEX or content-related checklists? Med Teach. 2020;42:578–84. https://doi.org/10.1080/0142159X.2020.1721449 . Pinilla S, Lerch S, Lüdi R, et al. Entrustment versus performance scale in high-stakes OSCEs: Rater insights and psychometric properties. Med Teach. 2023;45:885–92. https://doi.org/10.1080/0142159X.2023.2187683 . Regehr G, MacRae H, Reznick RK, et al. Comparing the psychometric properties of checklists and global rating scales for assessing performance on an OSCE-format examination. Acad Med. 1998;73:993–7. https://doi.org/10.1097/00001888-199809000-00020 . Mahmoud A. A Comparison of Checklist and Domain-Based Ratings in the Assessment of Objective Structured Clinical Examination (OSCE) Performance. Cureus 2023. https://doi.org/10.7759/cureus.40220 Daniels VJ, Pugh D. Twelve tips for developing an OSCE that measures what you want. Med Teach. 2018;40:1208–13. https://doi.org/10.1080/0142159X.2017.1390214 . Vetsch A, Berendonk C, Hari R. Reliabilität und Durchführbarkeit einer Ultraschallprüfung für ­Studierende in der Schweiz. Prim Hosp Care Médecine Interne Générale. 2020. https://doi.org/10.4414/phc-f.2020.10247 . Walzak A, Bacchus M, Schaefer JP, et al. Diagnosing Technical Competence in Six Bedside Procedures: Comparing Checklists and a Global Rating Scale in the Assessment of Resident Performance. Acad Med. 2015;90:1100–8. https://doi.org/10.1097/ACM.0000000000000704 . Ma IWY, Desy J, Woo MY, et al. Consensus-Based Expert Development of Critical Items for Direct Observation of Point-of-Care Ultrasound Skills. J Grad Med Educ. 2020;12:176–84. https://doi.org/10.4300/JGME-D-19-00531.1 . Lockyer J, Carraccio C, Chan M-K, et al. Core principles of assessment in competency-based medical education. Med Teach. 2017;39:609–16. https://doi.org/10.1080/0142159X.2017.1315082 . Tolsgaard MG, Todsen T, Sorensen JL, et al. International Multispecialty Consensus on How to Evaluate Ultrasound Competence: A Delphi Consensus Survey. PLoS ONE. 2013;8:e57687. https://doi.org/10.1371/journal.pone.0057687 . Chenot J-F, Simmenroth-Nayda A, Koch A, et al. Can student tutors act as examiners in an objective structured clinical examination? Med Educ. 2007;41:1032–8. https://doi.org/10.1111/j.1365-2923.2007.02895.x . Hari R, Kälin K, Birrenbach T, et al. Near-peer compared to faculty teaching of abdominal ultrasound for medical students – A randomized-controlled trial. Ultraschall Med - Eur J Ultrasound. 2024;45:77–83. https://doi.org/10.1055/a-2103-4787 . Räschle N, Hari R. «Blended Learning»-Basiskurs Sonografie: Im Peer-Tutoring zu einem SGUM-akkreditierten Ultraschall-Kurs. Praxis. 2018;107:1255–9. https://doi.org/10.1024/1661-8157/a003116 . Walter R, Räschle N, Blunier A-S, et al. Revision des e-Learnings zum Grundkurs Abdomen der Young Sonographers. Praxis. 2022;111:495–501. https://doi.org/10.1024/1661-8157/a003907 . Woehr DJ, Huffcutt AI. Rater training for performance appraisal: A quantitative review. J Occup Organ Psychol. 1994;67:189–205. https://doi.org/10.1111/j.2044-8325.1994.tb00562.x . Tavakol M, Dennick R. Making sense of Cronbach’s alpha. Int J Med Educ. 2011;2:53–5. https://doi.org/10.5116/ijme.4dfb.8dfd . Li M, Shavelson RJ, Yin Y, et al. Generalizability Theory. In: Cautin RL, Lilienfeld SO, editors. Encycl. Clin. Psychol. 1st ed. Wiley; 2015. pp. 1–19. https://doi.org/10.1002/9781118625392.wbecp352 . Additional Declarations No competing interests reported. Supplementary Files Appendixfinal.docx Cite Share Download PDF Status: Published Journal Publication published 14 Mar, 2026 Read the published version in BMC Medical Education → Version 1 posted Editorial decision: Revision requested 21 Jan, 2026 Reviews received at journal 13 Jan, 2026 Reviewers agreed at journal 13 Jan, 2026 Reviews received at journal 22 Dec, 2025 Reviewers agreed at journal 15 Dec, 2025 Reviewers agreed at journal 15 Dec, 2025 Reviewers invited by journal 11 Dec, 2025 Editor assigned by journal 11 Dec, 2025 Submission checks completed at journal 10 Dec, 2025 First submitted to journal 10 Dec, 2025 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. 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(OSCE) are a common examination format for ultrasound courses as it captures its manifold facets best [\u003cspan additionalcitationids=\"CR2 CR3 CR4\" citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e–\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. Their organisation, administration, and execution demand substantial knowledge, experience, and planning [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e, \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. The creation of the OSCE rating scale is a key component of this process [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. OSCE assessment requires rating scales to capture as much information as possible within limited observation time. While generic rating scales have shown advantages for less technical skills [\u003cspan additionalcitationids=\"CR9\" citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e–\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e], it remains debated whether this also applies to technical skills such as ultrasound [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eAccordingly, recent literature on ultrasound exams generally distinguishes into two options: task-specific (i.e. extended) rating scales [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e] and generic rating scales [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. Both types of rating scales, generic and extended, seem to provide advantages [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. Supporters of extended rating scales argue they offer greater reliability and validity [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. In contrast, supporters of a generic rating scale contend that the use of elaborate and procedure-specific tools may not always provide a better estimate of performance than scales relying on general competencies [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e, \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]. Furthermore, generic rating scales are easier to develop and can be quickly adapted to new OSCE stations, thereby simplifying OSCE organisation and coordination. Despite those facts, there has never been a head-to-head comparison of a generic and a task-specific extended rating scale for abdominal ultrasound; they have all been proposed and validated individually.\u003c/p\u003e \u003cp\u003eIn this study, we aimed to compare a generic and an extended rating scale evaluating the same domains. We investigated whether and how ratings differed in terms of internal consistency, generalisability and performance scores when using an extended versus a generic rating scale for assessing performance in basic abdominal ultrasound skills.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e\u003c/p\u003e "},{"header":"Methods","content":"\u003cp\u003eContext\u003c/p\u003e\u003cp\u003eThe Young Sonographers, a subsection of the Swiss Society for Ultrasound in Medicine (SGUM/SSUM), have provided courses on basic abdominal ultrasound since 2019, comprising five hours of e-learning and 16 hours of practical training taught by near-peer tutors [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e, \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]. Following a revision of this course on abdominal ultrasound [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e], existing OSCE stations and their task-specific extended rating scales [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e] were updated accordingly (unpublished thesis, available on request). Based on this revision, two rating scales were compared: one extended, task-specific version and one generic scale using the same domains.\u003c/p\u003e\u003cp\u003eStudy design\u003c/p\u003e\u003cp\u003eThis single-centre cohort study was conducted at the University of Bern to compare both rating scales in the abdominal ultrasound OSCE. Eighty medical students (3rd − 6th year; 65% female; mean age 23 years) who had completed the course and consented to participation were included. The study aimed to examine how both rating scales (extended, task-specific vs. generic) performed in terms of internal consistency and generalisability. Performance scores P (expressed as percentages out of a maximum of 30 points) were analysed across the rating scales, series and stations to better understand the sources of variability.\u003c/p\u003e\u003cp\u003eOSCE organisation\u003c/p\u003e\u003cp\u003eTwelve OSCE stations each assessed one course’s learning objective through a 4-minute practical ultrasound task. Stations were combined into eight six-station series (A1–D2; Appendix 1). Participants alternated between examiner and model roles.\u003c/p\u003e\u003cp\u003eEach series was completed by two groups (n = 11/12) of participants, except for Series D1 and D2, which were completed by only one group each (n = 6).\u003c/p\u003e\u003cp\u003eRating scales\u003c/p\u003e\u003cp\u003eBoth rating scales used identical domains - image settings, transducer handling, examination technique, image interpretation, and overall performance - with a total of 30 points. The extended scale contained task-specific descriptors (Appendix 2), whereas the generic scale provided brief, uniform prompts (Fig.\u0026nbsp;1).\u003c/p\u003e\n \u003cdiv align=\"left\"\u003eData collection\u003c/div\u003e\n\u003c/div\u003e\n\u003cp\u003eEach OSCE station was rated by two assessors simultaneously, one using each scale. Assessors were randomly assigned and included two SGUM-accredited physicians and 23 trained near-peer tutors with ≥ 1 year of teaching experience. Separate information sessions were held for each group, which conveyed basics for the OSCE itself and the study format, as well as instructions on how to use the rating scales, followed by two exemplary training videos to ensure a common frame of reference [22]. For each video existed an optimal rating score proposed by experienced ultrasound assessors, which was used to lay ground for the discussion among the raters.\u003c/p\u003e\n\u003ch2\u003eStatistical Analysis\u003c/h2\u003e\n\u003cp\u003eTo evaluate reliability, internal consistency was quantified using Cronbach's α, as a number between 0 and 1 [23]. We considered a Cronbach’s α of 0.7 and greater suitable for summative examinations such as ours [3, 12]. Generalisability Theory (GT) was used to assess the influence of non-rating scale-related factors on the final score (i.e. how much of variance was explained by the participants performance). Phi coefficients were calculated, ranging from 0 to 1, with 1 meaning that 100% of the variance in scores is due to the respondent's performance [24]. Phi coefficients were calculated for the following model: “(examinee : series) x station”. A Phi coefficient of 0.5 or higher was deemed adequate for this OSCE.\u003c/p\u003e\n\u003cp\u003eA multivariate split-plot ANOVA was used to examine how rating scale (within-subjects), series and OSCE station (between-subjects) contributed to variability in performance scores (α ≤ 0.05). Effect sizes were reported using partial η\u003csup\u003e2\u003c/sup\u003e, interpreted as small (\u0026lt; 0.07), medium (0.07–0.14) and strong (\u0026gt; 0.14). The level of significance was adapted using a post-hoc Bonferroni correction. If relevant differences between rating scales were found, paired-sample t-tests were performed to identify responsible OSCE stations.\u003c/p\u003e\n\u003cp\u003eAll statistical analyses were performed using SPSS for Windows, version 28 (IBM, Armonk, NY, USA). For the Generalizability Theory (GT), G_String, version 2019 was used.\u003c/p\u003e\n\u003cp\u003eEthical considerations\u003c/p\u003e\n\u003cp\u003eThe ethics committee of Bern, Switzerland declared this study was exempt from the Human Research Act and the need for a comprehensive ethical appraisal (BASEC number Req-2023-00254). Collected data was treated after being anonymised. All participants were informed about the study, then provided informed consent to participate or could withdraw without consequences on them receiving the course certificate. This study was conducted in accordance with the ethical principles outlined in the Declaration of Helsinki.\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003eInternal consistency and generalisability of rating scales\u003c/p\u003e\n\u003cp\u003eOur results indicate that internal consistency was higher for the generic scale than for the extended rating scale (Cronbach\u0026rsquo;s \u0026alpha; 0.803 vs 0.699; p\u0026thinsp;=\u0026thinsp;0.011, Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e). Across stations, Cronbach\u0026rsquo;s \u0026alpha; ranged from 0.50\u0026ndash;0.83 for the extended and 0.74\u0026ndash;0.87 for the generic scale (Appendix 4). The generalisability analysis (GT) revealed a Phi coefficient of 0.466 for the extended and 0.529 for the generic scale (Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e), suggesting that generic scales show greater performance-related variance than extended scales. The main results for both rating scales are indicated in Table \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e\n\u003cp\u003e\u003cimg 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\"\u003e\u003c/p\u003e\n\u003cp\u003eInfluence of rating scale on examinees\u0026rsquo; performance\u003c/p\u003e\n\u003cp\u003eIn multivariate testing, using a split-plot ANOVA we found a significant interaction between type of rating scale and performance P (F(1, 461)\u0026thinsp;=\u0026thinsp;22.95, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001, \u0026eta;\u0026sup2; = 0.047; small effect), in other words the two rating scales rated participants\u0026rsquo; performance significantly different with the generic rating scale attributing lower scores than the extended scale.\u003c/p\u003e\n\u003cp\u003eAdditionally, \u0026ldquo;series\u0026rdquo; and \u0026ldquo;station\u0026rdquo; showed significant effects (F(7, 461)\u0026thinsp;=\u0026thinsp;2.22, p\u0026thinsp;=\u0026thinsp;0.032, \u0026eta;\u0026sup2; = 0.033; F(11, 461)\u0026thinsp;=\u0026thinsp;3.95, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001, \u0026eta;\u0026sup2; = 0.086; small\u0026ndash;medium effects) on participants\u0026rsquo; performance P. Though, a significant interaction between type of rating scale and station was found (F(11, 461)\u0026thinsp;=\u0026thinsp;2.782, p\u0026thinsp;=\u0026thinsp;0.002, \u0026eta;\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;0.062, small effect size), the interaction between type of rating scales and series was not significant (p\u0026thinsp;=\u0026thinsp;0.108), suggesting the difference between scales was balanced across the 12 series.\u003c/p\u003e\n\u003cp\u003eFinally, we tested individual OSCE stations between rating scales to identify whether all stations are responsible for the significant overall difference using paired samples t-tests. Analyses yielded statistically significant differences between participants\u0026rsquo; performance P only for the stations \u0026ldquo;retroperitoneum\u0026rdquo; and \u0026ldquo;hepatic veins\u0026rdquo; (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001, Appendix 3).\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eIn this single-centre cohort study, we evaluated whether a generic and an extended rating scale for abdominal ultrasound skills yield comparable ratings on examinees\u0026rsquo; performance in OSCE. We found that a generic rating scale rated participants\u0026rsquo; performance with a higher internal consistency and generalisability, while attributing lower rating scores overall.\u003c/p\u003e \u003cp\u003eOur findings are aligned with broader medical education literature questioning the superiority of task-specific rating scales in performance-based exams. Early on, Regehr et al. showed that global rating scales demonstrate higher reliability and validity than checklists [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e], mirroring our observation that the generic scale produced more consistent ratings. Studies comparing generic domain-based versus extended checklist-based OSCE assessments likewise report that generic ratings better discriminate between levels of competence [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. Although some authors argue that generic ratings scales may be less appropriate for technical skills [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e], our results show that generic rating scales can also yield reliable assessments in a highly technical domain such as ultrasound.\u003c/p\u003e \u003cp\u003eWithin ultrasound education, our results complement the ongoing debate on the usefulness of detailed, task-specific rating scales. Hofer et al. advocated for extended scales to capture procedural complexity [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. However, in our study, extended scales produced inflated scores particularly in anatomically complex stations (hepatic veins, retroperitoneum), suggesting that extensive rating scales may inadvertently mask performance differences. More recent work supports moving away from highly granular rating scales: Ma et al. identified critical point-of-care ultrasound rating items but recommended restricting assessment to essential behaviours [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e], and Tolsgaard et al. emphasised evaluation of core domains, such as image optimisation, systematic examination, and interpretation, rather than long lists of procedural steps [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]. Our direct comparison provides empirical support for this shift, indicating that generic scales may better capture ultrasound performance by focusing on broader domains that reflect the integrated nature of the skill.\u003c/p\u003e \u003cp\u003eTaken together, our findings suggest that generic rating scales may be a suitable choice when designing OSCE assessments for basic abdominal ultrasound. They may simplify the development of new stations and reduce examiner training burden by avoiding the creation of detailed, station-specific rating scales. Further research should explore whether these findings extend to less technical areas of medical education and whether faculty and near-peer assessors perform differently when using generic scales.\u003c/p\u003e \u003cp\u003eStrength and limitations\u003c/p\u003e \u003cp\u003eThis study is the first to directly compare two rating scale formats for abdominal ultrasound OSCE assessment. To minimise cross-contamination of rating styles between assessors, we separated examiners into different break rooms and rotated them regularly. Our use of a diverse examiner group, including faculty and trained near-peer tutors, reflects real-world practice and supports generalisability for technical skills examination such as ultrasound. However, because only a small proportion of examiners were faculty, subgroup analysis was not feasible; results might differ in an assessor cohort composed entirely of faculty. Transferability to other OSCE contexts may also be limited by the narrow and well-defined task of abdominal ultrasound, where assessors share a relatively uniform mental model of optimal performance; conditions that may not hold in broader clinical domains.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eFor abdominal ultrasound OSCEs, generic rating scales appear to offer more stringent rating behaviour, higher internal consistency, and greater performance-related variance than extended scales. A well-designed generic rating scale can reliably and efficiently assess basic abdominal ultrasound competence, potentially offering greater generalisability and simpler implementation than extended, task-specific rating scales.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cp\u003eOSCE Objective structured clinical examinations\u003c/p\u003e\n\u003cp\u003eSGUM/SSUM Swiss society for ultrasound in medicine\u003c/p\u003e\n\u003cp\u003eANOVA Analysis of variance\u003c/p\u003e\n\u003cp\u003eGT Generalizability theory\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003eEthical considerations and consent to participation\u003c/p\u003e\n\u003cp\u003eThe ethics committee of Bern, Switzerland declared this study was exempt from the Human Research Act and the need for a comprehensive ethical appraisal (BASEC number Req-2023-00254). Collected data was treated after being anonymised. All participants were informed about the study, then provided informed consent to participate or could withdraw without consequences on them receiving the course certificate. This study was conducted in accordance with the ethical principles outlined in the Declaration of Helsinki.\u003c/p\u003e\n\u003cp\u003eConflicts of Interest\u003c/p\u003e\n\u003cp\u003eThe authors declare that they have no competing interests.\u003c/p\u003e\n\u003cp\u003eAuthors\u0026rsquo; contributions\u003c/p\u003e\n\u003cp\u003eSS, CB, DS and RW contributed to the conception of the work. RW, SS, CB and DS contributed to the design of the work. SS and RW did the acquisition, and DS performed the primary analysis. SS, DS, CB and RW interpreted the data. SS wrote the main manuscript and CB, DS, JB and RW substantively revised it. All authors have read and approved the final manuscript.\u003c/p\u003e\n\u003cp\u003eConsent to publication\u003c/p\u003e\n\u003cp\u003eAll authors agreed to the publication.\u003c/p\u003e\n\u003cp\u003eManuscript funding\u003c/p\u003e\n\u003cp\u003eThis work did not receive external funding.\u003c/p\u003e\n\u003cp\u003eAvailability of data and materials\u003c/p\u003e\n\u003cp\u003eThe used data are available from the corresponding author upon request.\u003c/p\u003e\n\u003cp\u003eAcknowledgements\u003c/p\u003e\n\u003cp\u003eWe would like to thank all the students, near-peer tutors and faculty tutors who participated in this study.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eBailitz J, O\u0026rsquo;Brien J, McCauley M, et al. Development of an expert consensus checklist for emergency ultrasound. AEM Educ Train. 2022;6:e10783. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1002/aet2.10783\u003c/span\u003e\u003cspan address=\"10.1002/aet2.10783\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBell C, Hall AK, Wagner N, et al. The Ultrasound Competency Assessment Tool (UCAT): Development and Evaluation of a Novel Competency-based Assessment Tool for Point‐of‐care Ultrasound. AEM Educ Train. 2021;5:e10520. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1002/aet2.10520\u003c/span\u003e\u003cspan address=\"10.1002/aet2.10520\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHofer M, Kamper L, Sadlo M, et al. Evaluation of an OSCE Assessment Tool for Abdominal Ultrasound Courses. Ultraschall Med - Eur J Ultrasound. 2011;32:184\u0026ndash;90. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1055/s-0029-1246049\u003c/span\u003e\u003cspan address=\"10.1055/s-0029-1246049\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eNorcini J, Anderson B, Bollela V et al. Criteria for good assessment: Consensus statement and recommendations from the Ottawa 2010 Conference. Med Teach. 2011;33:206\u0026ndash;14. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.3109/0142159X.2011.551559\u003c/span\u003e\u003cspan address=\"10.3109/0142159X.2011.551559\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eTeichgr\u0026auml;ber U, Ingwersen M, Ehlers C, et al. Integration of ultrasonography training into undergraduate medical education: catch up with professional needs. Insights Imaging. 2022;13:150. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1186/s13244-022-01296-3\u003c/span\u003e\u003cspan address=\"10.1186/s13244-022-01296-3\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKhan KZ, Gaunt K, Ramachandran S, Organisation, et al. Adm Med Teach. 2013;35:e1447\u0026ndash;63. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.3109/0142159X.2013.818635\u003c/span\u003e\u003cspan address=\"10.3109/0142159X.2013.818635\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMelcher P, Zajonz D, Roth A et al. Peer-assisted teaching student tutors as examiners in an orthopedic surgery OSCE station \u0026ndash; pros and cons. GMS Interdiscip Plast Reconstr Surg DGPW 5Doc17 2016. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.3205/IPRS000096\u003c/span\u003e\u003cspan address=\"10.3205/IPRS000096\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGiemsa P, W\u0026uuml;bbolding C, Fischer MR, et al. What works best in a general practice specific OSCE for medical students: Mini-CEX or content-related checklists? Med Teach. 2020;42:578\u0026ndash;84. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1080/0142159X.2020.1721449\u003c/span\u003e\u003cspan address=\"10.1080/0142159X.2020.1721449\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePinilla S, Lerch S, L\u0026uuml;di R, et al. Entrustment versus performance scale in high-stakes OSCEs: Rater insights and psychometric properties. Med Teach. 2023;45:885\u0026ndash;92. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1080/0142159X.2023.2187683\u003c/span\u003e\u003cspan address=\"10.1080/0142159X.2023.2187683\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRegehr G, MacRae H, Reznick RK, et al. Comparing the psychometric properties of checklists and global rating scales for assessing performance on an OSCE-format examination. Acad Med. 1998;73:993\u0026ndash;7. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1097/00001888-199809000-00020\u003c/span\u003e\u003cspan address=\"10.1097/00001888-199809000-00020\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMahmoud A. A Comparison of Checklist and Domain-Based Ratings in the Assessment of Objective Structured Clinical Examination (OSCE) Performance. Cureus 2023. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.7759/cureus.40220\u003c/span\u003e\u003cspan address=\"10.7759/cureus.40220\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDaniels VJ, Pugh D. Twelve tips for developing an OSCE that measures what you want. Med Teach. 2018;40:1208\u0026ndash;13. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1080/0142159X.2017.1390214\u003c/span\u003e\u003cspan address=\"10.1080/0142159X.2017.1390214\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eVetsch A, Berendonk C, Hari R. Reliabilit\u0026auml;t und Durchf\u0026uuml;hrbarkeit einer Ultraschallpr\u0026uuml;fung f\u0026uuml;r \u0026shy;Studierende in der Schweiz. Prim Hosp Care M\u0026eacute;decine Interne G\u0026eacute;n\u0026eacute;rale. 2020. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.4414/phc-f.2020.10247\u003c/span\u003e\u003cspan address=\"10.4414/phc-f.2020.10247\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWalzak A, Bacchus M, Schaefer JP, et al. Diagnosing Technical Competence in Six Bedside Procedures: Comparing Checklists and a Global Rating Scale in the Assessment of Resident Performance. Acad Med. 2015;90:1100\u0026ndash;8. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1097/ACM.0000000000000704\u003c/span\u003e\u003cspan address=\"10.1097/ACM.0000000000000704\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMa IWY, Desy J, Woo MY, et al. Consensus-Based Expert Development of Critical Items for Direct Observation of Point-of-Care Ultrasound Skills. J Grad Med Educ. 2020;12:176\u0026ndash;84. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.4300/JGME-D-19-00531.1\u003c/span\u003e\u003cspan address=\"10.4300/JGME-D-19-00531.1\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLockyer J, Carraccio C, Chan M-K, et al. Core principles of assessment in competency-based medical education. Med Teach. 2017;39:609\u0026ndash;16. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1080/0142159X.2017.1315082\u003c/span\u003e\u003cspan address=\"10.1080/0142159X.2017.1315082\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eTolsgaard MG, Todsen T, Sorensen JL, et al. International Multispecialty Consensus on How to Evaluate Ultrasound Competence: A Delphi Consensus Survey. PLoS ONE. 2013;8:e57687. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1371/journal.pone.0057687\u003c/span\u003e\u003cspan address=\"10.1371/journal.pone.0057687\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eChenot J-F, Simmenroth-Nayda A, Koch A, et al. Can student tutors act as examiners in an objective structured clinical examination? Med Educ. 2007;41:1032\u0026ndash;8. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1111/j.1365-2923.2007.02895.x\u003c/span\u003e\u003cspan address=\"10.1111/j.1365-2923.2007.02895.x\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHari R, K\u0026auml;lin K, Birrenbach T, et al. Near-peer compared to faculty teaching of abdominal ultrasound for medical students \u0026ndash; A randomized-controlled trial. Ultraschall Med - Eur J Ultrasound. 2024;45:77\u0026ndash;83. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1055/a-2103-4787\u003c/span\u003e\u003cspan address=\"10.1055/a-2103-4787\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eR\u0026auml;schle N, Hari R. \u0026laquo;Blended Learning\u0026raquo;-Basiskurs Sonografie: Im Peer-Tutoring zu einem SGUM-akkreditierten Ultraschall-Kurs. Praxis. 2018;107:1255\u0026ndash;9. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1024/1661-8157/a003116\u003c/span\u003e\u003cspan address=\"10.1024/1661-8157/a003116\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWalter R, R\u0026auml;schle N, Blunier A-S, et al. Revision des e-Learnings zum Grundkurs Abdomen der Young Sonographers. Praxis. 2022;111:495\u0026ndash;501. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1024/1661-8157/a003907\u003c/span\u003e\u003cspan address=\"10.1024/1661-8157/a003907\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWoehr DJ, Huffcutt AI. Rater training for performance appraisal: A quantitative review. J Occup Organ Psychol. 1994;67:189\u0026ndash;205. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1111/j.2044-8325.1994.tb00562.x\u003c/span\u003e\u003cspan address=\"10.1111/j.2044-8325.1994.tb00562.x\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eTavakol M, Dennick R. Making sense of Cronbach\u0026rsquo;s alpha. Int J Med Educ. 2011;2:53\u0026ndash;5. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.5116/ijme.4dfb.8dfd\u003c/span\u003e\u003cspan address=\"10.5116/ijme.4dfb.8dfd\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLi M, Shavelson RJ, Yin Y, et al. Generalizability Theory. In: Cautin RL, Lilienfeld SO, editors. Encycl. Clin. Psychol. 1st ed. Wiley; 2015. pp. 1\u0026ndash;19. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1002/9781118625392.wbecp352\u003c/span\u003e\u003cspan address=\"10.1002/9781118625392.wbecp352\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"bmc-medical-education","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"meed","sideBox":"Learn more about [BMC Medical Education](http://bmcmededuc.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/meed/default.aspx","title":"BMC Medical Education","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Ultrasound education, clinical evaluation, assessment techniques, rating scales, near-peer teaching, near-peer assessment","lastPublishedDoi":"10.21203/rs.3.rs-8305983/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8305983/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eBackground\u003c/p\u003e\n\u003cp\u003eObjective Structured Clinical Examinations (OSCE) are a widely used tool for assessing ultrasound competence, yet the optimal format for its rating scales remains debated. Extended, task-specific rating scales may offer detailed guidance, while more generic scales offer broader applicability and ease of use. This study compares whether and how ratings differed when using an extended versus a generic rating scale for assessing performance in basic abdominal ultrasound skills.\u003c/p\u003e\n\u003cp\u003eMethods\u003c/p\u003e\n\u003cp\u003eIn this single-centre cohort study, 80 medical students participated in an OSCE for abdominal ultrasound rated by both an extended and generic rating scale in parallel. Five domains were evaluated: image settings, transducer handling, examination technique, image explanation and overall performance. Each OSCE station was rated by two assessors simultaneously, one using each scale.\u003c/p\u003e\n\u003cp\u003eResults\u003c/p\u003e\n\u003cp\u003eThe generic rating scale demonstrated significantly higher internal consistency (mean Cronbach’s α generic 0.803 vs. extended 0.699; p=0.011) and a higher generalisability (Phi generic 0.529 vs. extended 0.466). Absolute ratings on the generic rating scale were significantly lower (performance P generic 75.38% vs. extended 77.99%, p\u0026lt;0.001), though in-between stations difficulty was only significantly different for two stations.\u003c/p\u003e\n\u003cp\u003eConclusion\u003c/p\u003e\n\u003cp\u003eIn total, the generic rating scale showed a more stringent rating with a higher internal consistency and a higher variance due to participants’ performance.\u003c/p\u003e\n\u003cp\u003eTherefore, a well-designed generic rating scale can reliably and efficiently assess basic abdominal ultrasound performance, possibly offering greater generalisability and simpler implementation than extended task specific rating scales.\u003c/p\u003e","manuscriptTitle":"Abdominal Ultrasound Performance Assessment: A Comparison of Generic and Extended OSCE Rating Scales","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-12-22 08:51:46","doi":"10.21203/rs.3.rs-8305983/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2026-01-22T04:16:44+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-01-13T21:01:47+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"65443386899388017829677441989319084339","date":"2026-01-13T20:56:30+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-12-22T21:34:20+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"86810328273540991008572030757417258814","date":"2025-12-15T16:54:31+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"280912717978411083225838761987321298435","date":"2025-12-15T08:10:19+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-12-11T18:06:43+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-12-11T10:03:47+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-12-10T12:18:43+00:00","index":"","fulltext":""},{"type":"submitted","content":"BMC Medical Education","date":"2025-12-10T12:10:23+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"bmc-medical-education","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"meed","sideBox":"Learn more about [BMC Medical Education](http://bmcmededuc.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/meed/default.aspx","title":"BMC Medical Education","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"cc4679ae-96ed-459b-aaed-5f8cac948c17","owner":[],"postedDate":"December 22nd, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2026-03-16T16:05:47+00:00","versionOfRecord":{"articleIdentity":"rs-8305983","link":"https://doi.org/10.1186/s12909-026-08994-2","journal":{"identity":"bmc-medical-education","isVorOnly":false,"title":"BMC Medical Education"},"publishedOn":"2026-03-14 16:00:05","publishedOnDateReadable":"March 14th, 2026"},"versionCreatedAt":"2025-12-22 08:51:46","video":"","vorDoi":"10.1186/s12909-026-08994-2","vorDoiUrl":"https://doi.org/10.1186/s12909-026-08994-2","workflowStages":[]},"version":"v1","identity":"rs-8305983","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8305983","identity":"rs-8305983","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}