{"doi":"10.1002/lio2.70332","title":"Ability to Adjust Head Position in a <scp>3D</scp> ‐Printed Flexible Nasolaryngoscopy Model Improves Fidelity and Trainee Experience","abstract":"Objectives: To assess the utility of a novel 3D-printed model incorporating user-directed head position adjustments for flexible fiberoptic nasolaryngoscopy (FNL) training and simulation. Methods: This proof-of-concept study utilized a CT-based, 3D-printed airway model permitting adjustments in head protrusion, flexion, and extension, with associated anatomical changes in oropharyngeal shape. Cervical flexion and atlantoaxial extension (\"sniffing position\") represented the optimal head position (OHP) for laryngeal visualization, as determined by attending faculty otolaryngologists. During FNL trials, trainees simulated patient instruction for head adjustment and were asked to indicate their perceived OHP. Standardized photographs of trainees' OHP were taken and compared by training level using fiducial marker-based image analysis. Surveys evaluated trainee experience. Results: A total of 26 medical students and residents (PGY-1 to PGY-4), completed FNL trials. Senior residents (R3+) showed little variability in their chosen OHP. While intermediate learners (R1-R2) showed the greatest variability in OHP there were no significant differences in final OHP among participants. Trainees rated the helpfulness of positional adjustments 8 ± 1.80 on a 10-point Likert scale. Conclusions: Head position maneuverability improves fidelity and the FNL training experience. Using the model, participants at all training levels were able to achieve OHPs comparable with experienced practitioners. Greater OHP variability among those with moderate experience suggests this model feature may be used by trainees to optimize technique. This novel model presents an affordable, portable, and easily replicable tool to enhance FNL simulation and training.","journal":"Laryngoscope Investigative Otolaryngology","year":2025,"id":585606,"datarank":0.0,"base_score":0.0,"endowment":0.0,"self_citation_contribution":0.0,"citation_network_contribution":0.0,"self_endowment_contribution":0.0,"citer_contribution":0.0,"corpus_percentile":null,"corpus_rank":null,"citation_count":0,"citer_count":0,"citers_with_citation_signal":0,"citers_with_endowment":0,"datacite_reuse_total":0,"is_dataset":false,"is_dataset_confidence":0.9596,"is_data_producer":false,"deposit_databanks":null,"is_oa":true,"file_count":0,"downloads":0,"has_version_chain":false,"published_date":"2025-01-01","fair_score":null,"fair_percentile":null,"algorithm_id":"datarank_citation_only_1hop_v6","ranking_scope":"data_only","authors":[{"id":1422351,"name":"Anna Christina Clements","orcid":"0000-0002-6869-3339","position":1,"is_corresponding":false},{"id":1422353,"name":"Michael Bindschadler","orcid":"0000-0002-9275-2845","position":2,"is_corresponding":false},{"id":428151,"name":"Ezgi Mercan","orcid":"0000-0001-6920-048X","position":3,"is_corresponding":false},{"id":1422354,"name":"Huy Viet Le","orcid":"0000-0001-5579-2875","position":4,"is_corresponding":false},{"id":942229,"name":"Tanya K. Meyer","orcid":"0000-0002-1112-2674","position":5,"is_corresponding":false},{"id":294716,"name":"Seth D. Friedman","orcid":"0000-0001-8785-7215","position":6,"is_corresponding":false},{"id":1422355,"name":"Maya G. Sardesaı","orcid":"0000-0001-9670-4931","position":7,"is_corresponding":false},{"id":1422352,"name":"Felix E. Fernández‐Penny","orcid":"0000-0003-1577-8141","position":0,"is_corresponding":true}],"reference_count":3,"raw_metadata":null,"created_at":"2026-07-19T02:59:24.273134Z","pmid":"41868823","pmcid":null,"fwci":null,"citation_percentile":null,"influential_citations":0,"oa_status":null,"license":null,"views":0,"total_file_size_bytes":0,"version_count":0,"fair_f":null,"fair_a":null,"fair_i":null,"fair_r":null,"fair_zscore":null,"fair_rationale":null,"fair_model":null,"fair_agent_version":null,"fair_fulltext_source":null,"fair_has_llm":null,"fair_computed_at":null,"clinical_trials":[],"software_tools":[],"db_accessions":[],"linked_datasets":[],"topics":[]}