Drawing to Understand: Feasibility and Perceived Benefits of a Clerkship-Based Surgical Anatomy Drawing Session

Drawing to Understand: Feasibility and Perceived Benefits of a Clerkship-Based Surgical Anatomy Drawing Session | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Drawing to Understand: Feasibility and Perceived Benefits of a Clerkship-Based Surgical Anatomy Drawing Session Takehiko Hanaki, Rumiko Omatsu, Kyoichi Kihara, Yoshiyuki Fujiwara, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8780505/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 6 You are reading this latest preprint version Abstract Background Clinicians widely use explanatory diagrams to convey complex surgical anatomy and postoperative arrangements. However, undergraduate medical curricula rarely provide structured opportunities for students to practice this skill. Hence, learners may struggle to integrate anatomical knowledge into coherent spatial representations for clinical explanation. This study aimed to conduct an initial perception-focused evaluation of a clerkship-based drawing session, examining learners’ perceived importance of surgical anatomy diagrams and confidence in producing them. Methods In a single-institution repeated cross-sectional pre/post educational evaluation within one clerkship cohort, 30 medical students (5th year, n = 22; 6th year, n = 8) participated in a 90–120-min small-group session (2–6 learners) between September 2025 and December 2025. The session included a baseline drawing without references, an instructor-led live drawing with optional computed tomography imaging, redrawing under the same conditions, and individualized feedback emphasizing anatomical accuracy and spatial coherence. Anonymous pre- and post-session questionnaires were administered to assess perceived importance and confidence using 5-point Likert scales. Post-session items were used to evaluate perceived improvement and overall satisfaction. Because the questionnaires were anonymous, pre- and post-session distributions were compared as independent samples using the Mann–Whitney U test. Results Approximately 83.3% of participants reported no prior experience drawing gastrointestinal reconstruction diagrams or organ diagrams depicting spatial relationships. Ratings of perceived importance (median [interquartile ranges (IQR)]: 4 [ 4 – 5 ] vs 5 [4.25–5]; p = 0.014) and confidence (2 [ 1 – 2 ] vs 4 [ 3 – 4 ]; p < 0.001) were significantly higher after the session than pre-session ratings. Effect sizes were r = 0.277 for importance and r = 0.645 for confidence. Ratings of perceived improvement and overall satisfaction were high (median [IQR]: 4.5 [ 4 – 5 ] and 5 [ 4 – 5 ], respectively). Paired drawings illustrated common baseline misconceptions and, after redrawing, more coherent spatial representations. Conclusions A structured drawing session during surgical clerkship was feasible and associated with higher post-session ratings of perceived importance and confidence in creating surgical anatomy diagrams. Future studies should incorporate linked pre- and post-assessments, rubric-based scoring, and multi-institutional replication to determine whether these perceived gains translate into improved diagram accuracy and explanatory quality. Trial registration Not applicable. Drawing-based learning Surgical anatomy education Clinical clerkship Learner confidence Medical education Figures Figure 1 Figure 2 Figure 3 Background A clear understanding of anatomical structures and surgical procedures is essential for surgeons and medical students. In clinical practice, explanatory diagrams, which are widely used to depict complex spatial relationships among organs, vessels, and postoperative structures [ 1 – 3 ], are not primarily artistic products. Rather, they are cognitive tools that externalize understanding and render clinical reasoning visible [ 4 – 6 ]. If well designed, they convey spatial relationships, continuity, and procedural change efficiently, often more effectively than verbal description alone [ 7 , 8 ]. This is particularly relevant in gastrointestinal surgery, where postoperative anatomy can be counterintuitive and clear diagrams support interdisciplinary communication and patient explanations. These diagrams may also facilitate clinical reasoning by making the structure of an explanation easier to examine [ 3 , 9 ]. The diagrams must represent essential relationships, such as adjacency, continuity, and vascular territories, which are determined by clinicians; however, this skill is rarely taught systematically in undergraduate curricula [ 10 – 12 ]. Instead, medical students typically learn anatomy using observation-based approaches, including textbooks, atlases, lectures, and cadaver dissection [ 11 , 13 ]. The abovementioned approaches provide only declarative knowledge and may not prepare learners to reorganize it as an explanatory representation for clinical communication. Consequently, students may struggle to identify and integrate organ relationships, vascular pathways, or postoperative configurations into a coherent spatial diagram, and may instead recall them as isolated facts. Thus, the resulting diagrams may be fragmented or internally inconsistent, which can reduce learners’ confidence in explaining anatomy using diagrams that they draw. In numerous clerkship settings, opportunities to practice explanatory communication are restricted by time pressures and supervision, and even motivated learners may receive limited feedback on the accuracy of their spatial understanding [ 14 – 16 ]. Drawing activities can address this gap by making learners’ representations explicit and by providing concrete targets for feedback, such as anatomical misconceptions, anterior–posterior reversals, and inconsistencies in continuity [ 17 – 19 ]. From a learning-science perspective, drawing is often considered a generative activity that supports knowledge integration, wherein learners select key structures, encode relationships, and attempt to maintain internal consistency within a representation [ 20 – 23 ]. However, this process can increase cognitive load, particularly for beginners [ 24 – 26 ]. To balance challenge and support, a structured sequence comprising initial independent drawing, instructor demonstration, and guided redrawing has been recommended to provide effective scaffolding while preserving the diagnostic value of baseline drawings [ 27 – 29 ]. Previous reports of drawing-based interventions have occasionally used broad labels such as “active learning” without clarifying the representational problems that were identified and addressed [ 30 , 31 ]. Regarding endpoints, appropriate targets for feedback include misconceptions [ 32 ], spatial errors [ 18 ], and limited integration observed in baseline drawings of surgical anatomy [ 33 ]. However, they are not validated endpoints in the absence of standardized scoring and supporting evidence of validity [ 34 , 35 ]. Clerkship-based programs must also be feasible within limited time and resources [ 36 , 37 ]. Drawing sessions are compelling because they require minimal equipment and allow individualized feedback. Nevertheless, feasibility alone does not demonstrate educational effectiveness. During the initial evaluation, we assessed learners’ perceived importance of surgical anatomy diagrams and confidence in creating them, collected data on the post-session ratings of perceived improvement and satisfaction, and documented representative examples of misconceptions and their revision. To address the gap in the structured integration of drawing into surgical clerkships, a structured drawing-based session was implemented to help learners create explanatory surgical anatomy diagrams depicting organ relationships and vascular territories. Furthermore, pre- and post-session ratings of perceived importance and confidence were examined, and data on the post-session ratings of perceived improvement and overall satisfaction were collected. Paired student drawings were reviewed qualitatively to illustrate typical misconceptions and their revisions, rather than as objective performance outcomes. The current study aimed to conduct an initial evaluation focusing on learners’ perceptions before and after a structured drawing-based session. Methods Study design and setting This study was conducted as a repeated cross-sectional pre/post educational evaluation within one clerkship cohort at Tottori University Faculty of Medicine. Primary outcomes were learner-reported perceptions; paired drawings were reviewed qualitatively as illustrative educational artifacts and were not used as scored or inferential performance endpoints. The session was delivered between September 2025 and December 2025 as part of routine educational activities. Paired drawings were retained for qualitative illustration only; consent procedures are described below. The examination was designed pragmatically to fit clerkship constraints, with minimal disruption to routine education. Participants The participants were medical students enrolled in the clinical clerkship who attended the drawing-based session during the study period. During the sessions, which were conducted in small groups (2 to 6 students), instructors reviewed the drawings and provided brief individualized feedback. Completion of the questionnaire was voluntary, with assurance that nonparticipation would not affect clerkship evaluations, and no incentives were offered to students. This was a convenience sample based on clerkship enrollment, and a formal sample size calculation was not performed. Eligibility criteria were medical students enrolled in the clinical clerkship at Tottori University Faculty of Medicine during the study period who attended the scheduled drawing-based session. Students who did not attend the session or did not submit the anonymous questionnaire were not included in the questionnaire analysis. No additional exclusion criteria were applied. Ethical considerations and reporting The Tottori University Ethical Review Board (Institutional Review Board [IRB] reference no. 25A179; IRB approval no. 11000653) reviewed and approved this evaluation protocol. Questionnaire participation was voluntary, with responses collected anonymously and analyzed in a deidentified format. Student drawings were treated as educational artifacts obtained during routine teaching. For publication use of drawings, verbal consent was obtained at the end of each session; only de-identified drawings from consenting students were eligible for inclusion. This study is reported in accordance with the STROBE statement for observational studies; the completed STROBE checklist is provided as Supplementary Material 1. Drawing-based educational session (procedure) Each session lasted approximately 90–120 min and followed a consistent sequence intended to elicit learners’ baseline representations, provide a shared anatomical framework, and support revision via redrawing and feedback. All sessions were led by a single surgeon-educator (T.H.) to maintain consistency in providing instruction and feedback. The session comprised the following phases: (1) a pre-session questionnaire; (2) a brief orientation that framed drawing as a tool for clinical reasoning and communication; (3) a baseline drawing (approximately 10 min) completed without reference materials and without peer viewing; (4) an instructor demonstration with real-time drawing projected to the group, with computed tomography scans used as needed to clarify spatial relationships such as anterior–posterior depth and left–right positioning (e.g., the spleen on the left lateral and posterior aspects of the stomach, and the superior mesenteric artery [SMA] and vein anterior to the third portion of the duodenum); (5) a redraw under the same no-reference and no-peer-viewing restrictions; (6) brief individualized feedback focusing on anatomical correctness, spatial relationships, and internal consistency rather than artistic style; and (7) a post-session questionnaire. Drawing tasks Drawing themes were selected to reflect clinically relevant surgical anatomy, emphasizing organ relationships, vascular territories, and integrated spatial organization. Typical tasks included diagrams of the upper abdominal organs (liver, stomach, spleen, pancreas, duodenum, jejunum, and colon), positional relationships among organs supplied by the celiac artery (CA) and the SMA, the hepatoduodenal ligament and intrahepatic vascular and biliary structures, lower gastrointestinal anatomy in relation to the inferior mesenteric artery, gastric vascular anatomy, the CA to SMA collateral pathways and arcades, and portal venous anatomy (the superior mesenteric vein, inferior mesenteric vein, and splenic vein converging into the portal vein). Depending on time and learner progress, approximately two themes were typically covered in a single session. Across themes, instruction discouraged isolated parts-only depictions and instead emphasized an integrated spatial representation that could be used for clinical explanation. Materials and drawing modality Most students drew on an electronic tablet; papers were used if a tablet was not available. Instructor demonstrations used live drawing projected to a monitor. Student drawings were compiled by a student representative and submitted in a deidentified format. To enable within-student pairing of pre- and post-session drawings for educational review, submissions were assigned with alphabetical labels (e.g., student A pre/post and student B pre/post). The student representative created these labels, which were not linkable to personal identifiers by the investigators. Selected paired drawings from consented students were included as illustrative examples. Questionnaire measures and administration A structured questionnaire in Japanese was administered immediately before and after the session through Google Forms (Google LLC, Mountain View, CA, USA), and responses were collected anonymously. Thus, individual-level linkage between pre- and post-session responses was not possible. An English translation of the items is provided in subsequent sections as a direct translation of the original wording, and no formal back-translation was performed. Baseline questionnaire (pre-session) • Year of study: “What is your current year of study?” (1st–3rd year, 4th year, 5th year, or 6th year) • Previous experience in drawing gastrointestinal reconstruction diagrams or organ diagrams that depict spatial relationships (4-point ordinal scale): “Have you ever drawn a gastrointestinal reconstruction diagram by yourself?” (1 = never; 2 = once during class; 3 = voluntary self-study; 4 = multiple times during training or practice) • Confidence (baseline): “How confident are you in drawing explanatory surgical anatomy diagrams (e.g., gastrointestinal reconstruction diagrams and organ relationships)?” (5-point Likert scale) • Importance (baseline): “How important do you think the ability to produce explanatory diagrams of surgical procedures and organ anatomy is for physicians and medical students?” (5-point Likert scale) • Motivation: “How motivated are you to learn to draw explanatory diagrams of gastrointestinal reconstruction and organ anatomy?” (5-point Likert scale) Post-session questionnaire • Importance: same as the baseline importance item (5-point Likert scale) • Perceived improvement: “Do you feel that your ability to draw explanatory diagrams of gastrointestinal reconstruction and organ anatomy improved through this session?” (5-point Likert scale) • Confidence: “At the end of the session, how confident are you in drawing explanatory surgical anatomy diagrams (e.g., gastrointestinal reconstruction diagrams and organ relationships)?” (5-point Likert scale) • Overall satisfaction: “Overall, how satisfied were you with this session?” (5-point Likert scale) The full English questionnaire is provided in Supplementary Material 2. Scale anchors All items used 5-point Likert scales. For items assessing importance, responses ranged from 1 (not necessary at all) to 5 (highly necessary). For items assessing confidence, responses ranged from 1 (not confident at all) to 5 (highly confident). For items assessing perceived improvement, responses ranged from 1 (strongly disagree) to 5 (strongly agree). For items assessing satisfaction, responses ranged from 1 (not satisfied at all) to 5 (highly satisfied). Higher scores indicated greater levels of the construct assessed. Data structure and handling Pre- and post-session questionnaires were administered to the same clerkship cohort during the session. However, responses were anonymized and could not be linked at the individual level. Therefore, pre- and post-session questionnaire datasets were analyzed as independent samples. In contrast, paired pre- and post-session drawings were available for a subset of consented students because within-student pairing was preserved using deidentified labels during compilation by a student representative. These paired drawings were used for qualitative illustration only and were not treated as inferential endpoints. Examples were selected to illustrate typical baseline misconceptions and how they were revised after the instructions were provided. They were not intended to examine cohort-wide prevalence or the degree of change. Questionnaire analyses were conducted as complete cases. Statistical analysis Likert-scale outcomes were summarized as medians with interquartile ranges (IQRs). The primary analyses compared two perception outcomes between the pre- and post-session questionnaires: perceived importance and confidence. Pre- and post-session distributions were compared as independent samples using the Mann–Whitney U test, as the responses could not be linked at the individual level. The effect sizes for the Mann–Whitney U tests were calculated as r, defined as the absolute Z value divided by the square root of the total sample size. Total sample size was defined as the total number of observations across the pre- and post-session groups combined. The post-session evaluation items, namely, perceived improvement and overall satisfaction, were summarized descriptively. All tests were two-sided, and statistical significance was set at p values <0.05. Analyses were performed in R (R Foundation for Statistical Computing, Vienna, Austria). The results were interpreted as group-level differences in response distributions rather than within-person change. Results Participants and baseline characteristics In total, 30 medical students completed the questionnaire, and all responses were suitable for analysis. There were no missing responses. All participants were in the clinical clerkship stage. In particular, 22 (73.3%) students were in the 5th year, and 8 (26.7%) were in the 6th year. There were no responses from 1st–4th-year students. Regarding previous experience, 25 (83.3%) students reported that they had never drawn gastrointestinal reconstruction diagrams. Four (13.3%) students reported drawing such a diagram once during a class, and one (3.3%) reported having experience with voluntary self-study. None of the participants reported drawing gastrointestinal reconstruction diagrams multiple times during training or practice. Table 1 shows the baseline characteristics of the participants. Table 2 and Figure 1 depict the pre- and post-session questionnaire outcomes. Table 1. Characteristics of the participants and results of the questionnaire survey. Pre- and post-session questionnaire survey scores were compared at the group level using the Mann–Whitney U test because individual responses could not be linked. Table 2. Questionnaire outcomes: pre- and post-comparisons and post-session evaluations Questionnaire outcomes (pre- vs post-session and independent-sample comparison) The pre- and post-session questionnaire outcomes are summarized in Table 2 and visualized in Figure 1 (n = 30 per group). The post-session ratings for both perceived importance and confidence were higher than the pre-session ratings. The ratings for perceived importance increased (median [IQR]: 4 [4–5] vs 5 [4.25–5]; p = 0.014, r = 0.277) (Figure 1A). The box plot showed a clear upward shift in the median, with a modest overlap between distributions, which indicated a small-to-moderate effect and a greater recognition of diagramming as professionally relevant. The ratings for confidence increased (median [IQR]: 2 [1–2] vs 4 [3–4]; p < 0.001, r = 0.645) (Figure 1B). The minimal overlap between boxes and the large effect size reflected an educationally meaningful increase in perceived capability. Post-session evaluation items Table 2 presents the results of the post-session evaluations. The ratings for perceived improvement (median [IQR]: 4.5 [4–5]) and overall satisfaction (median: 5 [4–5]) were high. Figure 2 depicts the representative paired drawings from two consenting students, which were shown as qualitative examples of typical baseline misconceptions and their revision after instruction. Representative paired drawings (illustrative examples) Figure 2 shows the representative paired drawings from two students who provided verbal consent. Panels A and B were from one student (pre and post), and panels C and D were from another student (pre and post). In panels A and B, the pre-session diagram omitted the spleen and liver, placed the CA at an unusually cranial level, and depicted the SMA and superior mesenteric vein in anatomically inconsistent relationships to the stomach. In the post-session diagram, these elements were revised, with the omitted organs included and the vascular courses depicted more consistently with the surrounding anatomy. In panels C and D, the pre-session diagram, which included the duodenum and jejunum positioned anterior to the transverse colon, had a limited spatial integration. In the post-session diagram, these relationships were redrawn with a greater internal consistency, with the duodenum and jejunum placed posterior to the transverse colon, resulting in a more coherent overall arrangement. These paired drawings were presented as qualitative examples to illustrate typical baseline misconceptions and how they may be revised after providing instructions. They were not intended to examine inferential outcome measures. Discussion This study conducted an initial perception‑based evaluation of a clerkship‑based drawing session. The post‑session ratings for both the perceived importance of surgical anatomy diagrams and confidence in producing them were higher than the pre-session ratings. This finding indicated shifts in learners’ subjective appraisal rather than objective performance. Paired drawings from students who provided consent were presented only as exploratory qualitative examples to illustrate typical baseline misconceptions and how they were revised after providing instructions. They were not used as performance outcomes. Interpretation of perceived importance and confidence A key observation was the higher post‑session rating for the perceived importance of drawing surgical anatomical diagrams. Importance is a subjective construct and does not indicate competence [ 38 – 40 ]. Nevertheless, it may be educationally informative because it reflects how learners appraise the professional relevance of diagramming as a clinical communication tool. In clerkship settings dominated by observation and time constraints, explicitly framing drawing as a practical method to convey spatial relationships may increase recognition that diagrams are integral to clinical explanation rather than as an optional study habit. If sustained, such a shift in appraisal could plausibly influence learners’ willingness to practice drawing in authentic contexts. However, downstream behaviors were not measured here [ 41 , 42 ]. The post-session ratings for confidence in drawing explanatory surgical anatomy diagrams also increased. This measure reflects perceived capability rather than the objective assessment of performance [ 43 ]. Confidence may be a useful proximal perception outcome because it can influence whether learners attempt to draw diagrams and seek feedback [ 44 ]. However, confidence is not a validated surrogate for explanatory competence [ 45 – 47 ]. Therefore, the results of the current study suggest a greater perceived readiness to draw, not evidence of reliable accuracy in clinical settings. The participants may have left the session with a clearer approach to diagram construction. However, without an objective assessment, it remains uncertain whether a higher confidence corresponds to measurable gains in accuracy, completeness, or explanatory clarity [ 47 ]. The degree of change differed between outcomes, with a small-to-moderate effect size for perceived importance (r = 0.277) and a larger effect size for confidence (r = 0.645). Hence, confidence may be more sensitive than perceived importance to a short, structured drawing-based instruction. This pattern is in accordance with that of previous research showing that confidence often responds more strongly to targeted practice than perceived relevance, particularly if learners engage in generative activities such as drawing [ 20 , 43 – 47 ]. These findings underscore the potential value of integrating structured diagramming exercises into clerkships, not only to enhance perceived capability but also to normalize the use of diagrams as a professional communication tool. How the drawing-based session may function educationally Figure 3 . Conceptual model of drawing-based learning in surgical anatomy. The model summarizes the instructional sequence used in this study: a baseline drawing to externalize learners’ internal representations, an instructor demonstration to provide a shared anatomical framework, a redraw to support revision and integration, and brief individualized feedback focused on anatomical correctness and spatial relationships. The model was presented as an educational rationale rather than as causal evidence. In this model, the baseline drawing externalizes learners’ representations. The instructor demonstration provides a shared framework, and redraw with feedback supports revision. The model is offered as an educational rationale, not as causal evidence. The session combined a baseline drawing under no-reference and no-peer-viewing conditions, an instructor demonstration with imaging support as needed, a redraw, and a brief individualized feedback focusing on anatomical correctness and spatial consistency rather than artistic style [ 48 , 49 ]. Educationally, the sequence is intended to externalize learners’ spatial reasoning, make misconceptions observable, and enable targeted feedback followed by revision through redraw [ 20 , 50 ]. Figure 2 shows the illustrative examples of misconceptions and their revision in selected cases. Role of the representative paired drawings Figure 2 is included to make the instructional process concrete by showing typical baseline misconceptions and spatial inconsistencies in student diagrams and how these were revised after providing instructions in paired examples, thereby validating the targets of feedback. The figure is illustrative and is not intended to quantify prevalence, support generalization, or serve as a performance endpoint [ 51 ]. The drawings were not scored using a standardized rubric with blinded ratings. Thus, the frequency of similar revisions across the cohort and the extent to which revisions generalized across themes cannot be determined from these examples [ 52 ]. Practical implications for clerkship teaching The session fits within a 90–120-min clerkship slot, and it is suitable for small-group teaching [ 53 ]. It only requires basic materials such as tablets or paper, and the instructor demonstration can be adjusted to learners’ baseline representations. In practice, this format can be incorporated into routine clerkship teaching and may support learners’ perceived importance of surgical anatomy diagrams and confidence in drawing them [ 43 ]. Methodological considerations and limitations The current study had several limitations that should be considered. First, questionnaire responses were anonymized and could not be linked within individuals. Therefore, pre- and post-session results were analyzed as independent samples and should not be interpreted as within-person change. Second, this single-institution evaluation during one clerkship period had a modest sample size, which limits generalizability. Third, the outcomes relied on self-reported Likert-scale ratings, and they may be influenced by response bias, including social desirability and expectancy effects. Fourth, the post-session ratings showed clustering at the upper end of the scale, consistent with potential ceiling effects. Fifth, although paired drawings were available, they were not assessed using a standardized rubric with blinded raters. Hence, objective conclusions about diagram accuracy or integration across the cohort cannot be drawn. Sixth, the session was delivered by a single instructor, and instructor-specific effects could not be excluded. Finally, the session combined several instructional elements, and the contribution of each component could not be separated in this design. Future directions Future work should strengthen measurement and study design while preserving feasibility. First, enabling anonymous pre- and post-session linkage would allow paired analyses and the assessment of individual differences, such as previous drawing experience. Second, drawings should be evaluated objectively using a rubric aligned with session goals, with blinded scoring and inter-rater reliability reporting. Third, retention and transfer should be assessed using delayed follow-up tasks and by testing whether learners can apply diagram drawing to novel anatomical regions or simulated scenarios. Finally, comparative designs, replication across instructors, and multi-institutional implementation would improve external validity and confirm whether changes in learner perceptions correspond to measurable gains in diagram accuracy and explanatory competence. Conclusions This clerkship‑based drawing session was feasible and associated with higher post-session ratings of the perceived importance of surgical anatomy diagrams and confidence in creating them. These outcomes reflect learners’ subjective perceptions. However, they do not indicate objective improvements in diagram accuracy or explanatory quality. Representative paired drawings showed typical baseline misconceptions and their revision in selected cases. Nevertheless, they were not used as performance measures. Future studies incorporating linked pre- and postassessments, rubric‑based scoring, multi‑institutional replication, and longitudinal follow‑up should be performed to determine whether the perceived gains observed here translate into sustained improvements in learners’ explanatory diagramming skills. Abbreviations Institutional Review Board, IRB; IQR, interquartile range; CA, Celiac artery; SMA, Superior mesenteric artery Declarations Ethics approval and consent to participate This study was conducted as an educational evaluation within the clinical clerkship at Tottori University Faculty of Medicine and was approved by the Tottori University Ethical Review Board (IRB reference no. 25A179; IRB approval no. 11000653). Questionnaire data originally collected during routine educational activities were retrospectively analyzed for research purposes. For this retrospective analysis, the requirement for individual informed consent was waived by the IRB, and an opt-out approach was implemented in accordance with institutional ethics policy. Students were informed that participation was optional and would not affect clerkship assessment. Responses were collected without direct personal identifiers, and all data had already been anonymized before the research team accessed them for analysis. Consent for publication For publication use of drawings, the instructor (T.H.) obtained verbal consent at the end of each session. Before consent was requested, students were informed that publication use was voluntary, that refusal would not affect their clerkship assessment, and that only de-identified drawings would be used. Consent status was documented in a session-level log maintained by the instructor. Drawings were de-identified at the time of image acquisition and converted to pair-only labels (e.g., pre/post within the same student) without personal identifiers. Thus, only within-student pre/post pairing was retained, and no direct individual identification was possible from the research dataset. Only de-identified drawings from students with documented verbal consent were eligible for inclusion in this article. Availability of data and materials The data were collected anonymously and contain no direct personal identifiers. The datasets are not publicly available due to institutional restrictions, but de-identified data may be obtained from the corresponding author on reasonable request, subject to approval by the Tottori University Ethical Review Board. Reporting guideline This study is reported in accordance with the STROBE statement for observational studies; the completed STROBE checklist is provided as Supplementary Material 1. Competing interests The authors declare no competing interests related to this study. Funding This research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors. Authors’ contributions T.H. conceived and designed the study, developed the research concept, delivered the clerkship-based drawing session and provided instruction and feedback, conducted the analyses, and drafted the manuscript. R.O. contributed to manuscript drafting and prepared all documents related to the institutional ethical review. K.K. coordinated the clinical clerkship operations and contributed to manuscript writing. Y.F. and M.U. supervised the study, provided critical revisions, and approved the final manuscript. All authors reviewed and approved the final manuscript and consented to its submission. 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The impact of multiple representations on students' understanding of vector field concepts: Implementation of simulations and sketching activities into lecture-based recitations in undergraduate physics. Front Psychol. 2025;16:1544764. Stephens G, Lazarus M. Anatomical MythBusters: An approach to addressing misconceptions in foundational sciences education. Med Educ. 2024;58(11):1395–6. Card E, Mauch J, Lin I. Learner Drawing and Sculpting in Surgical Education: A Systematic Review. J Surg Res. 2021;267:577–85. Downing SM. Validity: on meaningful interpretation of assessment data. Med Educ. 2003;37(9):830–7. Kane MT. Validating the Interpretations and Uses of Test Scores. J Educ Meas. 2013;50(1):1–73. Burgess A, Van Diggele C, Roberts C, Mellis C. Facilitating small group learning in the health professions. BMC Med Educ. 2020;20(Suppl 2):457. Cendan J, Silver M, Ben-David K. Changing the student clerkship from traditional lectures to small group case-based sessions benefits the student and the faculty. J Surg Educ. 2011;68 2:117–20. Gabbard T, Romanelli F. The Accuracy of Health Professions Students’ Self-Assessments Compared to Objective Measures of Competence. Am J Pharm Educ. 2021;85(4):8405. Stranovská E, Lalinská M, Boboňová I. Perception of the Degree of Importance of Teacher’s Professional Competences from the Perspective of Teacher and Head Teacher in the Evaluation Process of Educational Efficiency. 2017, 127:5–20. Haverila M, Haverila K, McLaughlin C. Competence-based appraisal of the quality of higher education using the importance-performance (IPMA) analysis. J Mark High Educ. 2023;35:446–70. Card E, Lejbman J, Trueblood E, Bennett B, Dunham B, Swain A, DeLisser H. Drawing for Visual Communication in Medicine: A Novel Pilot Course for Senior Medical Students. J Surg Educ. 2021;79(2):389–96. Lyon P, Letschka P, Ainsworth T, Haq I. Drawing Pedagogies in Higher Education: the Learning Impact of a Collaborative Cross-disciplinary Drawing Course. Int J Art Des Educ. 2018;37:221–32. Bandura A. Self-efficacy: the exercise of control. W.H. Freeman; 1997. Ben Yehuda M, Murphy RA, Le Pelley ME, Navarro DJ, Yeung N. Confidence regulates feedback processing during human probabilistic learning. J experimental Psychol Gen. 2025;154(1):80–95. Gabbard T, Romanelli PMF. Do Student Self-Assessments of Confidence and Knowledge Equate to Competence? Am J Pharm Educ. 2021;85:8405. Jagemann I, Thiele C, Von Brachel R, Hirschfeld G. Substituting confidence for competence in health literacy: a review of studies, citations, and trial registrations. Health Promot Int 2025, 40(1). Davis DA, Mazmanian PE, Fordis M, Van Harrison R, Thorpe KE, Perrier L. Accuracy of physician self-assessment compared with observed measures of competence: a systematic review. JAMA. 2006;296(9):1094–102. Schwendimann B, Linn M. Comparing two forms of concept map critique activities to facilitate knowledge integration processes in evolution education. J Res Sci Teach. 2016;53:70–94. Tytler R, Prain V, Kirk M, Mulligan J, Nielsen C, Speldewinde C, White P, Xu L. Characterising a Representation Construction Pedagogy for Integrating Science and Mathematics in the Primary School. Int J Sci Math Educ. 2022;21:1153–75. Quillin K, Thomas S. Drawing-to-Learn: A Framework for Using Drawings to Promote Model-Based Reasoning in Biology. CBE Life Sci Educ. 2015;14(1):es2. Standards for educational. and psychological testing. Washington, DC, US: American Psychological Association; 1985. Moskal B, Leydens J. Scoring Rubric Development: Validity and Reliability. Practical Assess Res Evaluation 2000, 7. Steinert Y. Student perceptions of effective small group teaching. Med Educ. 2004;38(3):286–93. Additional Declarations No competing interests reported. Supplementary Files Supplementarymaterial1.STROBEchecklist.doc SupplementaryMaterial2.Englishversionofthequestionnaire.pdf Cite Share Download PDF Status: Under Review Version 1 posted Reviewers agreed at journal 05 Mar, 2026 Reviewers invited by journal 05 Mar, 2026 Editor assigned by journal 04 Mar, 2026 Editor invited by journal 06 Feb, 2026 Submission checks completed at journal 06 Feb, 2026 First submitted to journal 06 Feb, 2026 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. We do this by developing innovative software and high quality services for the global research community. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-8780505","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":601211199,"identity":"fdfa9abf-005a-4edf-b833-0370d02e4a8c","order_by":0,"name":"Takehiko Hanaki","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA4ElEQVRIiWNgGAWjYBACAyBmbACRDMwHIFwGOIVfiwQPA1sCKVoYQFp48CpEAHP2swc/zihgqLOXPvNN6kbBHQb+9gMMxQV4tFj25CVLbgA5jC93m3SOwTMGiTMJDMYz8DnsQI6B5AOD/xI8PLwgLYcZGG4wMBjz4NNy/o3xzwcgW3h4noG1yBPUciPHDOIwHh42sBYDwlremFnOMGCQ7DnDZmwN1MJjeCaxAb9fzucY3+z5w8DP3sP88HbOn8NycscPHzPGF2IYAOgkxjZjUnSAAfNjkrWMglEwCkbBcAYAE5RCqO6T9ocAAAAASUVORK5CYII=","orcid":"","institution":"Tottori University","correspondingAuthor":true,"prefix":"","firstName":"Takehiko","middleName":"","lastName":"Hanaki","suffix":""},{"id":601211200,"identity":"f8ead7ce-3074-4bd2-8d4d-cbd4696783e9","order_by":1,"name":"Rumiko Omatsu","email":"","orcid":"","institution":"Tottori University","correspondingAuthor":false,"prefix":"","firstName":"Rumiko","middleName":"","lastName":"Omatsu","suffix":""},{"id":601211203,"identity":"8f7ef24f-623b-479e-bdad-ae44ee05c5ae","order_by":2,"name":"Kyoichi Kihara","email":"","orcid":"","institution":"Tottori University","correspondingAuthor":false,"prefix":"","firstName":"Kyoichi","middleName":"","lastName":"Kihara","suffix":""},{"id":601211204,"identity":"353b6557-3c44-44f4-8d35-3108d29366aa","order_by":3,"name":"Yoshiyuki Fujiwara","email":"","orcid":"","institution":"Tottori University","correspondingAuthor":false,"prefix":"","firstName":"Yoshiyuki","middleName":"","lastName":"Fujiwara","suffix":""},{"id":601211207,"identity":"477405ba-0e2a-41de-a4d7-84a2e1052c75","order_by":4,"name":"Masaru Ueki","email":"","orcid":"","institution":"Tottori University","correspondingAuthor":false,"prefix":"","firstName":"Masaru","middleName":"","lastName":"Ueki","suffix":""}],"badges":[],"createdAt":"2026-02-04 00:54:00","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-8780505/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-8780505/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":104374973,"identity":"ba5e2197-193f-48b3-851d-82d81d157fa6","added_by":"auto","created_at":"2026-03-11 06:11:27","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":39381,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eDistribution of questionnaire scores before and after the drawing-based session.\u003c/strong\u003e\u003cbr\u003e\nPanel A shows perceived importance of drawing surgical anatomy diagrams. Panel B indicates confidence in producing such diagrams. Box plots represent medians and interquartile ranges (IQRs), with individual responses shown as points.\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-8780505/v1/1af319eab423da9d5cb27fb1.png"},{"id":104374978,"identity":"794d2e82-944d-4ab3-9623-c2aacbd9812f","added_by":"auto","created_at":"2026-03-11 06:11:28","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":164787,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eRepresentative paired student drawings before and after the drawing-based session.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003ePanels A and B show the drawings from one student (A: pre‑session, B: post‑session). In panel A, the superior mesenteric artery is depicted in a spatial relationship inconsistent with the integrated upper abdominal anatomy. Panel B revises this depiction and presents a more coherent arrangement of abdominal structures. Panels C and D depict drawings from another student (C: pre‑session, D: post‑session). In panel C, gastrointestinal structures were drawn with internally inconsistent spatial relationships and limited integration. Panel D demonstrates a more integrated representation with corrected spatial relationships. All illustrations were reproduced with verbal consent and were provided as qualitative examples, not as inferential outcome measures.\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-8780505/v1/124792a60e083f61b6563a97.png"},{"id":104374974,"identity":"caffe7ee-d598-4efc-8c4a-19f6661399c0","added_by":"auto","created_at":"2026-03-11 06:11:27","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":162540,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eConceptual model of drawing-based learning in surgical anatomy.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe model summarizes the instructional sequence used in this study: a baseline drawing to externalize learners’ internal representations, an instructor demonstration to provide a shared anatomical framework, a redraw to support revision and integration, and brief individualized feedback focused on anatomical correctness and spatial relationships. The model was presented as an educational rationale rather than as causal evidence.\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-8780505/v1/56d55c074a27bd7b8e456b29.png"},{"id":104779750,"identity":"db8b6de4-6fd3-40ae-8453-79dd61504a3a","added_by":"auto","created_at":"2026-03-17 07:45:44","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1561328,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8780505/v1/862bda58-e429-4d5f-9b43-91d61dc6807c.pdf"},{"id":104374977,"identity":"2616869d-ad34-4b10-b9ee-b731bda415b2","added_by":"auto","created_at":"2026-03-11 06:11:27","extension":"doc","order_by":0,"title":"","display":"","copyAsset":false,"role":"supplement","size":96256,"visible":true,"origin":"","legend":"","description":"","filename":"Supplementarymaterial1.STROBEchecklist.doc","url":"https://assets-eu.researchsquare.com/files/rs-8780505/v1/ecc7166107f702873151e958.doc"},{"id":104374976,"identity":"7d259c31-37e3-4765-bb7f-de417c75a55b","added_by":"auto","created_at":"2026-03-11 06:11:27","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":613749,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryMaterial2.Englishversionofthequestionnaire.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8780505/v1/c1cfe2bb194e72e83d126f06.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Drawing to Understand: Feasibility and Perceived Benefits of a Clerkship-Based Surgical Anatomy Drawing Session","fulltext":[{"header":"Background","content":"\u003cp\u003eA clear understanding of anatomical structures and surgical procedures is essential for surgeons and medical students. In clinical practice, explanatory diagrams, which are widely used to depict complex spatial relationships among organs, vessels, and postoperative structures [\u003cspan additionalcitationids=\"CR2\" citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e], are not primarily artistic products. Rather, they are cognitive tools that externalize understanding and render clinical reasoning visible [\u003cspan additionalcitationids=\"CR5\" citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. If well designed, they convey spatial relationships, continuity, and procedural change efficiently, often more effectively than verbal description alone [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. This is particularly relevant in gastrointestinal surgery, where postoperative anatomy can be counterintuitive and clear diagrams support interdisciplinary communication and patient explanations. These diagrams may also facilitate clinical reasoning by making the structure of an explanation easier to examine [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThe diagrams must represent essential relationships, such as adjacency, continuity, and vascular territories, which are determined by clinicians; however, this skill is rarely taught systematically in undergraduate curricula [\u003cspan additionalcitationids=\"CR11\" citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. Instead, medical students typically learn anatomy using observation-based approaches, including textbooks, atlases, lectures, and cadaver dissection [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThe abovementioned approaches provide only declarative knowledge and may not prepare learners to reorganize it as an explanatory representation for clinical communication. Consequently, students may struggle to identify and integrate organ relationships, vascular pathways, or postoperative configurations into a coherent spatial diagram, and may instead recall them as isolated facts. Thus, the resulting diagrams may be fragmented or internally inconsistent, which can reduce learners\u0026rsquo; confidence in explaining anatomy using diagrams that they draw.\u003c/p\u003e \u003cp\u003eIn numerous clerkship settings, opportunities to practice explanatory communication are restricted by time pressures and supervision, and even motivated learners may receive limited feedback on the accuracy of their spatial understanding [\u003cspan additionalcitationids=\"CR15\" citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. Drawing activities can address this gap by making learners\u0026rsquo; representations explicit and by providing concrete targets for feedback, such as anatomical misconceptions, anterior\u0026ndash;posterior reversals, and inconsistencies in continuity [\u003cspan additionalcitationids=\"CR18\" citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]. From a learning-science perspective, drawing is often considered a generative activity that supports knowledge integration, wherein learners select key structures, encode relationships, and attempt to maintain internal consistency within a representation [\u003cspan additionalcitationids=\"CR21 CR22\" citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]. However, this process can increase cognitive load, particularly for beginners [\u003cspan additionalcitationids=\"CR25\" citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]. To balance challenge and support, a structured sequence comprising initial independent drawing, instructor demonstration, and guided redrawing has been recommended to provide effective scaffolding while preserving the diagnostic value of baseline drawings [\u003cspan additionalcitationids=\"CR28\" citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e].\u003c/p\u003e \u003cp\u003ePrevious reports of drawing-based interventions have occasionally used broad labels such as \u0026ldquo;active learning\u0026rdquo; without clarifying the representational problems that were identified and addressed [\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e, \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e]. Regarding endpoints, appropriate targets for feedback include misconceptions [\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e], spatial errors [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e], and limited integration observed in baseline drawings of surgical anatomy [\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e]. However, they are not validated endpoints in the absence of standardized scoring and supporting evidence of validity [\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e, \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e]. Clerkship-based programs must also be feasible within limited time and resources [\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e, \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e]. Drawing sessions are compelling because they require minimal equipment and allow individualized feedback. Nevertheless, feasibility alone does not demonstrate educational effectiveness. During the initial evaluation, we assessed learners\u0026rsquo; perceived importance of surgical anatomy diagrams and confidence in creating them, collected data on the post-session ratings of perceived improvement and satisfaction, and documented representative examples of misconceptions and their revision.\u003c/p\u003e \u003cp\u003eTo address the gap in the structured integration of drawing into surgical clerkships, a structured drawing-based session was implemented to help learners create explanatory surgical anatomy diagrams depicting organ relationships and vascular territories. Furthermore, pre- and post-session ratings of perceived importance and confidence were examined, and data on the post-session ratings of perceived improvement and overall satisfaction were collected. Paired student drawings were reviewed qualitatively to illustrate typical misconceptions and their revisions, rather than as objective performance outcomes. The current study aimed to conduct an initial evaluation focusing on learners\u0026rsquo; perceptions before and after a structured drawing-based session.\u003c/p\u003e"},{"header":"Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eStudy design and setting\u003c/h2\u003e \u003cp\u003eThis study was conducted as a repeated cross-sectional pre/post educational evaluation within one clerkship cohort at Tottori University Faculty of Medicine. Primary outcomes were learner-reported perceptions; paired drawings were reviewed qualitatively as illustrative educational artifacts and were not used as scored or inferential performance endpoints.\u003c/p\u003e \u003cp\u003eThe session was delivered between September 2025 and December 2025 as part of routine educational activities. Paired drawings were retained for qualitative illustration only; consent procedures are described below. The examination was designed pragmatically to fit clerkship constraints, with minimal disruption to routine education.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eParticipants\u003c/h3\u003e\n\u003cp\u003eThe participants were medical students enrolled in the clinical clerkship who attended the drawing-based session during the study period. During the sessions, which were conducted in small groups (2 to 6 students), instructors reviewed the drawings and provided brief individualized feedback. Completion of the questionnaire was voluntary, with assurance that nonparticipation would not affect clerkship evaluations, and no incentives were offered to students. This was a convenience sample based on clerkship enrollment, and a formal sample size calculation was not performed.\u003c/p\u003e \u003cp\u003eEligibility criteria were medical students enrolled in the clinical clerkship at Tottori University Faculty of Medicine during the study period who attended the scheduled drawing-based session. Students who did not attend the session or did not submit the anonymous questionnaire were not included in the questionnaire analysis. No additional exclusion criteria were applied.\u003c/p\u003e\n\u003ch3\u003eEthical considerations and reporting\u003c/h3\u003e\n\u003cp\u003eThe Tottori University Ethical Review Board (Institutional Review Board [IRB] reference no. 25A179; IRB approval no. 11000653) reviewed and approved this evaluation protocol. Questionnaire participation was voluntary, with responses collected anonymously and analyzed in a deidentified format. Student drawings were treated as educational artifacts obtained during routine teaching. For publication use of drawings, verbal consent was obtained at the end of each session; only de-identified drawings from consenting students were eligible for inclusion.\u003c/p\u003e \u003cp\u003eThis study is reported in accordance with the STROBE statement for observational studies; the completed STROBE checklist is provided as Supplementary Material 1.\u003c/p\u003e\n\u003ch3\u003eDrawing-based educational session (procedure)\u003c/h3\u003e\n\u003cp\u003eEach session lasted approximately 90\u0026ndash;120 min and followed a consistent sequence intended to elicit learners\u0026rsquo; baseline representations, provide a shared anatomical framework, and support revision via redrawing and feedback. All sessions were led by a single surgeon-educator (T.H.) to maintain consistency in providing instruction and feedback. The session comprised the following phases: (1) a pre-session questionnaire; (2) a brief orientation that framed drawing as a tool for clinical reasoning and communication; (3) a baseline drawing (approximately 10 min) completed without reference materials and without peer viewing; (4) an instructor demonstration with real-time drawing projected to the group, with computed tomography scans used as needed to clarify spatial relationships such as anterior\u0026ndash;posterior depth and left\u0026ndash;right positioning (e.g., the spleen on the left lateral and posterior aspects of the stomach, and the superior mesenteric artery [SMA] and vein anterior to the third portion of the duodenum); (5) a redraw under the same no-reference and no-peer-viewing restrictions; (6) brief individualized feedback focusing on anatomical correctness, spatial relationships, and internal consistency rather than artistic style; and (7) a post-session questionnaire.\u003c/p\u003e\n\u003ch3\u003eDrawing tasks\u003c/h3\u003e\n\u003cp\u003eDrawing themes were selected to reflect clinically relevant surgical anatomy, emphasizing organ relationships, vascular territories, and integrated spatial organization. Typical tasks included diagrams of the upper abdominal organs (liver, stomach, spleen, pancreas, duodenum, jejunum, and colon), positional relationships among organs supplied by the celiac artery (CA) and the SMA, the hepatoduodenal ligament and intrahepatic vascular and biliary structures, lower gastrointestinal anatomy in relation to the inferior mesenteric artery, gastric vascular anatomy, the CA to SMA collateral pathways and arcades, and portal venous anatomy (the superior mesenteric vein, inferior mesenteric vein, and splenic vein converging into the portal vein). Depending on time and learner progress, approximately two themes were typically covered in a single session. Across themes, instruction discouraged isolated parts-only depictions and instead emphasized an integrated spatial representation that could be used for clinical explanation.\u003c/p\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eMaterials and drawing modality\u003c/h2\u003e \u003cp\u003eMost students drew on an electronic tablet; papers were used if a tablet was not available. Instructor demonstrations used live drawing projected to a monitor. Student drawings were compiled by a student representative and submitted in a deidentified format. To enable within-student pairing of pre- and post-session drawings for educational review, submissions were assigned with alphabetical labels (e.g., student A pre/post and student B pre/post). The student representative created these labels, which were not linkable to personal identifiers by the investigators. Selected paired drawings from consented students were included as illustrative examples.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eQuestionnaire measures and administration\u003c/h3\u003e\n\u003cp\u003eA structured questionnaire in Japanese was administered immediately before and after the session through Google Forms (Google LLC, Mountain View, CA, USA), and responses were collected anonymously. Thus, individual-level linkage between pre- and post-session responses was not possible. An English translation of the items is provided in subsequent sections as a direct translation of the original wording, and no formal back-translation was performed.\u003c/p\u003e\n\u003ch3\u003eBaseline questionnaire (pre-session)\u003c/h3\u003e\n\u003cp\u003e• Year of study: “What is your current year of study?” (1st–3rd year, 4th year, 5th year, or 6th year)\u003c/p\u003e\n\u003cp\u003e• Previous experience in drawing gastrointestinal reconstruction diagrams or organ diagrams that depict spatial relationships (4-point ordinal scale): “Have you ever drawn a gastrointestinal reconstruction diagram by yourself?” (1 = never; 2 = once during class; 3 = voluntary self-study; 4 = multiple times during training or practice)\u003c/p\u003e\n\u003cp\u003e• Confidence (baseline): “How confident are you in drawing explanatory surgical anatomy diagrams (e.g., gastrointestinal reconstruction diagrams and organ relationships)?” (5-point Likert scale)\u003c/p\u003e\n\u003cp\u003e• Importance (baseline): “How important do you think the ability to produce explanatory diagrams of surgical procedures and organ anatomy is for physicians and medical students?” (5-point Likert scale)\u003c/p\u003e\n\u003cp\u003e• Motivation: “How motivated are you to learn to draw explanatory diagrams of gastrointestinal reconstruction and organ anatomy?” (5-point Likert scale)\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003ePost-session questionnaire\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e• Importance: same as the baseline importance item (5-point Likert scale)\u003c/p\u003e\n\u003cp\u003e• Perceived improvement: “Do you feel that your ability to draw explanatory diagrams of gastrointestinal reconstruction and organ anatomy improved through this session?” (5-point Likert scale)\u003c/p\u003e\n\u003cp\u003e• Confidence: “At the end of the session, how confident are you in drawing explanatory surgical anatomy diagrams (e.g., gastrointestinal reconstruction diagrams and organ relationships)?” (5-point Likert scale)\u003c/p\u003e\n\u003cp\u003e• Overall satisfaction: “Overall, how satisfied were you with this session?” (5-point Likert scale)\u003c/p\u003e\n\u003cp\u003eThe full English questionnaire is provided in Supplementary Material 2.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eScale anchors\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll items used 5-point Likert scales. For items assessing importance, responses ranged from 1 (not necessary at all) to 5 (highly necessary). For items assessing confidence, responses ranged from 1 (not confident at all) to 5 (highly confident). For items assessing perceived improvement, responses ranged from 1 (strongly disagree) to 5 (strongly agree). For items assessing satisfaction, responses ranged from 1 (not satisfied at all) to 5 (highly satisfied). Higher scores indicated greater levels of the construct assessed.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eData structure and handling\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003ePre- and post-session questionnaires were administered to the same clerkship cohort during the session. However, responses were anonymized and could not be linked at the individual level. Therefore, pre- and post-session questionnaire datasets were analyzed as independent samples.\u003c/p\u003e\n\u003cp\u003eIn contrast, paired pre- and post-session drawings were available for a subset of consented students because within-student pairing was preserved using deidentified labels during compilation by a student representative. These paired drawings were used for qualitative illustration only and were not treated as inferential endpoints. Examples were selected to illustrate typical baseline misconceptions and how they were revised after the instructions were provided. They were not intended to examine cohort-wide prevalence or the degree of change. Questionnaire analyses were conducted as complete cases.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eStatistical analysis\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eLikert-scale outcomes were summarized as medians with interquartile ranges (IQRs). The primary analyses compared two perception outcomes between the pre- and post-session questionnaires: perceived importance and confidence. Pre- and post-session distributions were compared as independent samples using the Mann–Whitney U test, as the responses could not be linked at the individual level. The effect sizes for the Mann–Whitney U tests were calculated as r, defined as the absolute Z value divided by the square root of the total sample size. Total sample size was defined as the total number of observations across the pre- and post-session groups combined. The post-session evaluation items, namely, perceived improvement and overall satisfaction, were summarized descriptively. All tests were two-sided, and statistical significance was set at p values \u0026lt;0.05. Analyses were performed in R (R Foundation for Statistical Computing, Vienna, Austria). The results were interpreted as group-level differences in response distributions rather than within-person change.\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003e\u003cstrong\u003e\u003cem\u003eParticipants and baseline characteristics\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eIn total, 30 medical students completed the questionnaire, and all responses were suitable for analysis. There were no missing responses. All participants were in the clinical clerkship stage. In particular, 22 (73.3%) students were in the 5th year, and 8 (26.7%) were in the 6th year. There were no responses from 1st\u0026ndash;4th-year students.\u003c/p\u003e\n\u003cp\u003eRegarding previous experience, 25 (83.3%) students reported that they had never drawn gastrointestinal reconstruction diagrams. Four (13.3%) students reported drawing such a diagram once during a class, and one (3.3%) reported having experience with voluntary self-study. None of the participants reported drawing gastrointestinal reconstruction diagrams multiple times during training or practice. Table 1 shows the baseline characteristics of the participants. Table 2 and Figure 1 depict the pre- and post-session questionnaire outcomes.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 1. Characteristics of the participants and results of the questionnaire survey.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cimg 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\" width=\"540\" height=\"394\"\u003e\u003c/strong\u003e\u003cbr\u003e\u003c/p\u003e\n\u003cp\u003ePre- and post-session questionnaire survey scores were compared at the group level using the Mann\u0026ndash;Whitney U test because individual responses could not be linked.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 2. Questionnaire outcomes: pre- and post-comparisons and post-session evaluations\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cimg 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≥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\" width=\"849\" height=\"249\"\u003e\u003c/strong\u003e\u003cbr\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eQuestionnaire outcomes (pre- vs post-session and independent-sample comparison)\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe pre- and post-session questionnaire outcomes are summarized in Table 2 and visualized in Figure 1 (n = 30 per group). The post-session ratings for both perceived importance and confidence were higher than the pre-session ratings. The ratings for perceived importance increased (median [IQR]: 4 [4\u0026ndash;5] vs 5 [4.25\u0026ndash;5]; p = 0.014, r = 0.277) (Figure 1A). The box plot showed a clear upward shift in the median, with a modest overlap between distributions, which indicated a small-to-moderate effect and a greater recognition of diagramming as professionally relevant. The ratings for confidence increased (median [IQR]: 2 [1\u0026ndash;2] vs 4 [3\u0026ndash;4]; p \u0026lt; 0.001, r = 0.645) (Figure 1B). The minimal overlap between boxes and the large effect size reflected an educationally meaningful increase in perceived capability.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003ePost-session evaluation items\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTable 2 presents the results of the post-session evaluations. The ratings for perceived improvement (median [IQR]: 4.5 [4\u0026ndash;5]) and overall satisfaction (median: 5 [4\u0026ndash;5]) were high. Figure 2 depicts the representative paired drawings from two consenting students, which were shown as qualitative examples of typical baseline misconceptions and their revision after instruction.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eRepresentative paired drawings (illustrative examples)\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eFigure 2 shows the representative paired drawings from two students who provided verbal consent. Panels A and B were from one student (pre and post), and panels C and D were from another student (pre and post).\u003c/p\u003e\n\u003cp\u003eIn panels A and B, the pre-session diagram omitted the spleen and liver, placed the CA at an unusually cranial level, and depicted the SMA and superior mesenteric vein in anatomically inconsistent relationships to the stomach. In the post-session diagram, these elements were revised, with the omitted organs included and the vascular courses depicted more consistently with the surrounding anatomy.\u003c/p\u003e\n\u003cp\u003eIn panels C and D, the pre-session diagram, which included the duodenum and jejunum positioned anterior to the transverse colon, had a limited spatial integration. In the post-session diagram, these relationships were redrawn with a greater internal consistency, with the duodenum and jejunum placed posterior to the transverse colon, resulting in a more coherent overall arrangement.\u003c/p\u003e\n\u003cp\u003eThese paired drawings were presented as qualitative examples to illustrate typical baseline misconceptions and how they may be revised after providing instructions. They were not intended to examine inferential outcome measures.\u003c/p\u003e\n\u003cp\u003e\u003cbr\u003e\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eThis study conducted an initial perception‑based evaluation of a clerkship‑based drawing session. The post‑session ratings for both the perceived importance of surgical anatomy diagrams and confidence in producing them were higher than the pre-session ratings. This finding indicated shifts in learners\u0026rsquo; subjective appraisal rather than objective performance. Paired drawings from students who provided consent were presented only as exploratory qualitative examples to illustrate typical baseline misconceptions and how they were revised after providing instructions. They were not used as performance outcomes.\u003c/p\u003e \u003cdiv id=\"Sec21\" class=\"Section2\"\u003e \u003ch2\u003eInterpretation of perceived importance and confidence\u003c/h2\u003e \u003cp\u003eA key observation was the higher post‑session rating for the perceived importance of drawing surgical anatomical diagrams. Importance is a subjective construct and does not indicate competence [\u003cspan additionalcitationids=\"CR39\" citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e]. Nevertheless, it may be educationally informative because it reflects how learners appraise the professional relevance of diagramming as a clinical communication tool. In clerkship settings dominated by observation and time constraints, explicitly framing drawing as a practical method to convey spatial relationships may increase recognition that diagrams are integral to clinical explanation rather than as an optional study habit. If sustained, such a shift in appraisal could plausibly influence learners\u0026rsquo; willingness to practice drawing in authentic contexts. However, downstream behaviors were not measured here [\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e, \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThe post-session ratings for confidence in drawing explanatory surgical anatomy diagrams also increased. This measure reflects perceived capability rather than the objective assessment of performance [\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e]. Confidence may be a useful proximal perception outcome because it can influence whether learners attempt to draw diagrams and seek feedback [\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e]. However, confidence is not a validated surrogate for explanatory competence [\u003cspan additionalcitationids=\"CR46\" citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e]. Therefore, the results of the current study suggest a greater perceived readiness to draw, not evidence of reliable accuracy in clinical settings. The participants may have left the session with a clearer approach to diagram construction. However, without an objective assessment, it remains uncertain whether a higher confidence corresponds to measurable gains in accuracy, completeness, or explanatory clarity [\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e]. The degree of change differed between outcomes, with a small-to-moderate effect size for perceived importance (r\u0026thinsp;=\u0026thinsp;0.277) and a larger effect size for confidence (r\u0026thinsp;=\u0026thinsp;0.645). Hence, confidence may be more sensitive than perceived importance to a short, structured drawing-based instruction.\u003c/p\u003e \u003cp\u003eThis pattern is in accordance with that of previous research showing that confidence often responds more strongly to targeted practice than perceived relevance, particularly if learners engage in generative activities such as drawing [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e, \u003cspan additionalcitationids=\"CR44 CR45 CR46\" citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e]. These findings underscore the potential value of integrating structured diagramming exercises into clerkships, not only to enhance perceived capability but also to normalize the use of diagrams as a professional communication tool.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec22\" class=\"Section2\"\u003e \u003ch2\u003eHow the drawing-based session may function educationally\u003c/h2\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eFigure \u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e. \u003cb\u003eConceptual model of drawing-based learning in surgical anatomy.\u003c/b\u003e\u003cdiv class=\"BlockQuote\"\u003e\u003cp\u003eThe model summarizes the instructional sequence used in this study: a baseline drawing to externalize learners\u0026rsquo; internal representations, an instructor demonstration to provide a shared anatomical framework, a redraw to support revision and integration, and brief individualized feedback focused on anatomical correctness and spatial relationships. The model was presented as an educational rationale rather than as causal evidence.\u003c/p\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003eIn this model, the baseline drawing externalizes learners\u0026rsquo; representations. The instructor demonstration provides a shared framework, and redraw with feedback supports revision. The model is offered as an educational rationale, not as causal evidence. The session combined a baseline drawing under no-reference and no-peer-viewing conditions, an instructor demonstration with imaging support as needed, a redraw, and a brief individualized feedback focusing on anatomical correctness and spatial consistency rather than artistic style [\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e, \u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e]. Educationally, the sequence is intended to externalize learners\u0026rsquo; spatial reasoning, make misconceptions observable, and enable targeted feedback followed by revision through redraw [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e, \u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e]. Figure\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e shows the illustrative examples of misconceptions and their revision in selected cases.\u003c/p\u003e \u003cdiv id=\"Sec23\" class=\"Section3\"\u003e \u003ch2\u003eRole of the representative paired drawings\u003c/h2\u003e \u003cp\u003eFigure \u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e is included to make the instructional process concrete by showing typical baseline misconceptions and spatial inconsistencies in student diagrams and how these were revised after providing instructions in paired examples, thereby validating the targets of feedback. The figure is illustrative and is not intended to quantify prevalence, support generalization, or serve as a performance endpoint [\u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e51\u003c/span\u003e]. The drawings were not scored using a standardized rubric with blinded ratings. Thus, the frequency of similar revisions across the cohort and the extent to which revisions generalized across themes cannot be determined from these examples [\u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e52\u003c/span\u003e].\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec24\" class=\"Section2\"\u003e \u003ch2\u003ePractical implications for clerkship teaching\u003c/h2\u003e \u003cp\u003eThe session fits within a 90\u0026ndash;120-min clerkship slot, and it is suitable for small-group teaching [\u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e53\u003c/span\u003e]. It only requires basic materials such as tablets or paper, and the instructor demonstration can be adjusted to learners\u0026rsquo; baseline representations. In practice, this format can be incorporated into routine clerkship teaching and may support learners\u0026rsquo; perceived importance of surgical anatomy diagrams and confidence in drawing them [\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e].\u003c/p\u003e \u003cdiv id=\"Sec25\" class=\"Section3\"\u003e \u003ch2\u003eMethodological considerations and limitations\u003c/h2\u003e \u003cp\u003eThe current study had several limitations that should be considered. First, questionnaire responses were anonymized and could not be linked within individuals. Therefore, pre- and post-session results were analyzed as independent samples and should not be interpreted as within-person change. Second, this single-institution evaluation during one clerkship period had a modest sample size, which limits generalizability. Third, the outcomes relied on self-reported Likert-scale ratings, and they may be influenced by response bias, including social desirability and expectancy effects. Fourth, the post-session ratings showed clustering at the upper end of the scale, consistent with potential ceiling effects. Fifth, although paired drawings were available, they were not assessed using a standardized rubric with blinded raters. Hence, objective conclusions about diagram accuracy or integration across the cohort cannot be drawn. Sixth, the session was delivered by a single instructor, and instructor-specific effects could not be excluded. Finally, the session combined several instructional elements, and the contribution of each component could not be separated in this design.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec26\" class=\"Section3\"\u003e \u003ch2\u003eFuture directions\u003c/h2\u003e \u003cp\u003eFuture work should strengthen measurement and study design while preserving feasibility. First, enabling anonymous pre- and post-session linkage would allow paired analyses and the assessment of individual differences, such as previous drawing experience. Second, drawings should be evaluated objectively using a rubric aligned with session goals, with blinded scoring and inter-rater reliability reporting. Third, retention and transfer should be assessed using delayed follow-up tasks and by testing whether learners can apply diagram drawing to novel anatomical regions or simulated scenarios. Finally, comparative designs, replication across instructors, and multi-institutional implementation would improve external validity and confirm whether changes in learner perceptions correspond to measurable gains in diagram accuracy and explanatory competence.\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e"},{"header":"Conclusions","content":"\u003cp\u003eThis clerkship‑based drawing session was feasible and associated with higher post-session ratings of the perceived importance of surgical anatomy diagrams and confidence in creating them. These outcomes reflect learners\u0026rsquo; subjective perceptions. However, they do not indicate objective improvements in diagram accuracy or explanatory quality. Representative paired drawings showed typical baseline misconceptions and their revision in selected cases. Nevertheless, they were not used as performance measures. Future studies incorporating linked pre- and postassessments, rubric‑based scoring, multi‑institutional replication, and longitudinal follow‑up should be performed to determine whether the perceived gains observed here translate into sustained improvements in learners\u0026rsquo; explanatory diagramming skills.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cp\u003eInstitutional Review Board, IRB; IQR, interquartile range; CA, Celiac artery; SMA, Superior mesenteric artery\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study was conducted as an educational evaluation within the clinical clerkship at Tottori University Faculty of Medicine and was approved by the Tottori University Ethical Review Board (IRB reference no. 25A179; IRB approval no. 11000653). Questionnaire data originally collected during routine educational activities were retrospectively analyzed for research purposes. For this retrospective analysis, the requirement for individual informed consent was waived by the IRB, and an opt-out approach was implemented in accordance with institutional ethics policy. Students were informed that participation was optional and would not affect clerkship assessment. Responses were collected without direct personal identifiers, and all data had already been anonymized before the research team accessed them for analysis.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eFor publication use of drawings, the instructor (T.H.) obtained verbal consent at the end of each session. Before consent was requested, students were informed that publication use was voluntary, that refusal would not affect their clerkship assessment, and that only de-identified drawings would be used. Consent status was documented in a session-level log maintained by the instructor.\u003c/p\u003e\n\u003cp\u003eDrawings were de-identified at the time of image acquisition and converted to pair-only labels (e.g., pre/post within the same student) without personal identifiers. Thus, only within-student pre/post pairing was retained, and no direct individual identification was possible from the research dataset. Only de-identified drawings from students with documented verbal consent were eligible for inclusion in this article.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe data were collected anonymously and contain no direct personal identifiers. The datasets are not publicly available due to institutional restrictions, but de-identified data may be obtained from the corresponding author on reasonable request, subject to approval by the Tottori University Ethical Review Board.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eReporting guideline\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study is reported in accordance with the STROBE statement for observational studies; the completed STROBE checklist is provided as Supplementary Material 1.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare no competing interests related to this study.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors’ contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eT.H. conceived and designed the study, developed the research concept, delivered the clerkship-based drawing session and provided instruction and feedback, conducted the analyses, and drafted the manuscript. R.O. contributed to manuscript drafting and prepared all documents related to the institutional ethical review. K.K. coordinated the clinical clerkship operations and contributed to manuscript writing. Y.F. and M.U. supervised the study, provided critical revisions, and approved the final manuscript.\u003c/p\u003e\n\u003cp\u003eAll authors reviewed and approved the final manuscript and consented to its submission.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors thank the participating medical students and the faculty members who supported the implementation of the sessions and data collection.\u003cstrong\u003e\u003cbr\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eMavroudis C, Lees G, Idriss R. Medical Illustration in the Era of Cardiac Surgery. 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The Drawing Principle in Multimedia Learning. \u003cem\u003eThe Cambridge Handbook of Multimedia Learning\u003c/em\u003e 2021.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMeter P, Stepanik N. Interventions to Support Learning from Multiple External Representations. 2020:76\u0026ndash;91.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSchnotz W, Bannert M. Construction and interference in learning from multiple representation. Learn Instruction. 2003;13:141\u0026ndash;56.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMeter P, Garner J. The Promise and Practice of Learner-Generated Drawing: Literature Review and Synthesis. Educational Psychol Rev. 2005;17:285\u0026ndash;325.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSchleinschok K, Eitel A, Scheiter K. Do drawing tasks improve monitoring and control during learning from text. Learn Instruction. 2017;51:10\u0026ndash;25.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWu S, Veen B, Rau M. How drawing prompts can increase cognitive engagement in an active learning engineering course. J Eng Educ. 2020;109:723\u0026ndash;42.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKohnle A, Ainsworth S, Passante G. Sketching to support visual learning with interactive tutorials. Phys Rev Phys Educ Res. 2020;16(2):020139.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eFiorella L, Stull A, Kuhlmann S, Mayer R. Instructor presence in video lectures: The role of dynamic drawings, eye contact, and instructor visibility. J Educ Psychol. 2018;111:1162\u0026ndash;71.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBaymetov B, Muratov XX. 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Characterising a Representation Construction Pedagogy for Integrating Science and Mathematics in the Primary School. Int J Sci Math Educ. 2022;21:1153\u0026ndash;75.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eQuillin K, Thomas S. Drawing-to-Learn: A Framework for Using Drawings to Promote Model-Based Reasoning in Biology. CBE Life Sci Educ. 2015;14(1):es2.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eStandards for educational. and psychological testing. Washington, DC, US: American Psychological Association; 1985.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMoskal B, Leydens J. Scoring Rubric Development: Validity and Reliability. Practical Assess Res Evaluation 2000, 7.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSteinert Y. Student perceptions of effective small group teaching. Med Educ. 2004;38(3):286\u0026ndash;93.\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":false,"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":"Drawing-based learning, Surgical anatomy education, Clinical clerkship, Learner confidence, Medical education","lastPublishedDoi":"10.21203/rs.3.rs-8780505/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8780505/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003eClinicians widely use explanatory diagrams to convey complex surgical anatomy and postoperative arrangements. However, undergraduate medical curricula rarely provide structured opportunities for students to practice this skill. Hence, learners may struggle to integrate anatomical knowledge into coherent spatial representations for clinical explanation. This study aimed to conduct an initial perception-focused evaluation of a clerkship-based drawing session, examining learners\u0026rsquo; perceived importance of surgical anatomy diagrams and confidence in producing them.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eIn a single-institution repeated cross-sectional pre/post educational evaluation within one clerkship cohort, 30 medical students (5th year, n\u0026thinsp;=\u0026thinsp;22; 6th year, n\u0026thinsp;=\u0026thinsp;8) participated in a 90\u0026ndash;120-min small-group session (2\u0026ndash;6 learners) between September 2025 and December 2025. The session included a baseline drawing without references, an instructor-led live drawing with optional computed tomography imaging, redrawing under the same conditions, and individualized feedback emphasizing anatomical accuracy and spatial coherence. Anonymous pre- and post-session questionnaires were administered to assess perceived importance and confidence using 5-point Likert scales. Post-session items were used to evaluate perceived improvement and overall satisfaction. Because the questionnaires were anonymous, pre- and post-session distributions were compared as independent samples using the Mann\u0026ndash;Whitney U test.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eApproximately 83.3% of participants reported no prior experience drawing gastrointestinal reconstruction diagrams or organ diagrams depicting spatial relationships. Ratings of perceived importance (median [interquartile ranges (IQR)]: 4 [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e] vs 5 [4.25\u0026ndash;5]; p\u0026thinsp;=\u0026thinsp;0.014) and confidence (2 [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e] vs 4 [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]; p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) were significantly higher after the session than pre-session ratings. Effect sizes were r\u0026thinsp;=\u0026thinsp;0.277 for importance and r\u0026thinsp;=\u0026thinsp;0.645 for confidence. Ratings of perceived improvement and overall satisfaction were high (median [IQR]: 4.5 [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e] and 5 [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e], respectively). Paired drawings illustrated common baseline misconceptions and, after redrawing, more coherent spatial representations.\u003c/p\u003e\u003ch2\u003eConclusions\u003c/h2\u003e \u003cp\u003eA structured drawing session during surgical clerkship was feasible and associated with higher post-session ratings of perceived importance and confidence in creating surgical anatomy diagrams. Future studies should incorporate linked pre- and post-assessments, rubric-based scoring, and multi-institutional replication to determine whether these perceived gains translate into improved diagram accuracy and explanatory quality.\u003c/p\u003e\u003ch2\u003eTrial registration\u003c/h2\u003e \u003cp\u003eNot applicable.\u003c/p\u003e","manuscriptTitle":"Drawing to Understand: Feasibility and Perceived Benefits of a Clerkship-Based Surgical Anatomy Drawing Session","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-03-11 06:11:22","doi":"10.21203/rs.3.rs-8780505/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"reviewerAgreed","content":"91750018654440189038870630050819731462","date":"2026-03-05T11:38:43+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2026-03-05T11:07:20+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2026-03-04T06:05:58+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2026-02-06T07:00:56+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2026-02-06T06:36:21+00:00","index":"","fulltext":""},{"type":"submitted","content":"BMC Medical Education","date":"2026-02-06T06:22:18+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":"15578419-716d-4bf9-991e-036683154399","owner":[],"postedDate":"March 11th, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2026-03-11T06:11:22+00:00","versionOfRecord":[],"versionCreatedAt":"2026-03-11 06:11:22","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8780505","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8780505","identity":"rs-8780505","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}