Olfactory Assessment for Affordable Parkinson Evaluation: Specificity, Enrichment, and the Cost of Follow-Up

Abstract Olfactory assessment is attractive as a comparatively low-burden component of Parkinson evaluation, but an inexpensive first test does not guarantee an affordable or clinically informative pathway. Smell loss has multiple causes, and the value of an abnormal result depends on the population, threshold, and subsequent assessment. This article combines a targeted synthesis of established and recent primary evidence with a reproducible hypothetical analysis of three olfactory operating points. Among 10,000 people with a specified target-condition prevalence of 1%, a sensitivity-oriented operating point with 90% sensitivity and 60% specificity generates 4,050 positive results, of which 90 are true positives. A more restrictive operating point with 60% sensitivity and 90% specificity generates 1,050 positives, of which 60 are true positives. Positive predictive values are 2.22% and 5.71%, respectively; neither supports standalone diagnosis. At the intermediate operating point, 80% sensitivity and specificity generate 2,060 follow-up assessments. An illustrative provider cost of five resource units for olfaction and sixty for follow-up produces 173,600 total units, while patient payments depend separately on the financing arrangement. A specificity-mixture analysis shows that applying a 90% specificity estimate to a population containing substantial non-Parkinson olfactory impairment can underestimate follow-up demand by 1,188 assessments. Recent abbreviated-test research supports investigation of shorter assessments, while recent work comparing Parkinson-related and non-Parkinson hyposmia cautions against assuming disease-specific odor patterns. The practical requirement is a staged pathway with appropriate comparison groups, valid local norms, transparent follow-up costs, and an alternative clinical route for persistent concerns. The numerical scenarios are hypothetical and do not establish the performance or price of any commercial test or platform. Article details NeuralCipherai; Kadir Tamrak; Salih Yaldız; Feride Yaldız; Ömer Ağyol; Yavuz Selim Silay; Hasan Randa Targeted evidence synthesis with hypothetical olfactory pathway analysis Three operating points, explicit patient-payment scenarios and a specificity-mixture analysis distinguish an inexpensive first test from an affordable informative assessment pathway. Project: NeuralCipher. Bibliographic records are supplied in BibTeX and RIS formats for reference-management software, including Zotero. Reproducible calculations and figure data accompany the article. References Berg, D., Postuma, R. B., Adler, C. H., Bloem, B. R., Chan, P., Dubois, B., Gasser, T., Goetz, C. G., Halliday, G., Joseph, L., Lang, A. E., Liepelt‐Scarfone, I., Litvan, I., Marek, K., Obeso, J., Oertel, W., Olanow, C. W., Poewe, W., Stern, M., & Deuschl, G. (2015). MDS research criteria for prodromal Parkinson's disease. Movement Disorders, 30(12), 1600–1611. https://doi.org/10.1002/mds.26431 Collins, G. S., Moons, K. G. M., Dhiman, P., Riley, R. D., Beam, A. L., Van Calster, B., Ghassemi, M., Liu, X., Reitsma, J. B., van Smeden, M., Boulesteix, A.-L., Camaradou, J. C., Celi, L. A., Denaxas, S., Denniston, A. K., Glocker, B., Golub, R. M., Harvey, H., Heinze, G., . . . Logullo, P. (2024). TRIPOD+AI statement: Updated guidance for reporting clinical prediction models that use regression or machine learning methods. BMJ, 385, Article e078378. https://doi.org/10.1136/bmj-2023-078378 Doty, R. L., Shaman, P., & Dann, M. (1984). Development of the university of pennsylvania smell identification test: A standardized microencapsulated test of olfactory function. Physiology & Behavior, 32(3), 489–502. https://doi.org/10.1016/0031-9384(84)90269-5 Haehner, A., Hummel, T., Hummel, C., Sommer, U., Junghanns, S., & Reichmann, H. (2007). Olfactory loss may be a first sign of idiopathic Parkinson's disease. Movement Disorders, 22(6), 839–842. https://doi.org/10.1002/mds.21413 Heinzel, S., Berg, D., Gasser, T., Chen, H., Yao, C., Postuma, R. B., & the MDS Task Force on the Definition of Parkinson's Disease. (2019). Update of the MDS research criteria for prodromal Parkinson's disease. Movement Disorders, 34(10), 1464–1470. https://doi.org/10.1002/mds.27802 Hummel, T., Kobal, G., Gudziol, H., & Mackay-Sim, A. (2007). Normative data for the “sniffin’ sticks” including tests of odor identification, odor discrimination, and olfactory thresholds: An upgrade based on a group of more than 3,000 subjects. European Archives of Oto-Rhino-Laryngology, 264(3), 237–243. https://doi.org/10.1007/s00405-006-0173-0 Hummel, T., Sekinger, B., Wolf, S. R., Pauli, E., & Kobal, G. (1997). ‘sniffin’ sticks': Olfactory performance assessed by the combined testing of odour identification, odor discrimination and olfactory threshold. Chemical Senses, 22(1), 39–52. https://doi.org/10.1093/chemse/22.1.39 Jennings, D., Siderowf, A., Stern, M., Seibyl, J., Eberly, S., Oakes, D., Marek, K., & PARS Investigators. (2017). Conversion to Parkinson disease in the PARS hyposmic and dopamine transporter-deficit prodromal cohort. JAMA Neurology, 74(8), 933–940. https://doi.org/10.1001/jamaneurol.2017.0985 Lee, K. J., Tilling, K. M., Cornish, R. P., Little, R. J. A., Bell, M. L., Goetghebeur, E., Hogan, J. W., & Carpenter, J. R. (2021). Framework for the treatment and reporting of missing data in observational studies: The Treatment And Reporting of Missing data in Observational Studies framework. Journal of Clinical Epidemiology, 134, 79–88. https://doi.org/10.1016/j.jclinepi.2021.01.008 Li, J., Grimes, K., Saade, J., Tomlinson, J. J., Mestre, T. A., Schade, S., Weber, S., Dakna, M., Wicke, T., Lang, E., Trenkwalder, C., Salmaso, N., Frank, A., Ramsay, T., Manuel, D., aSCENT-PD Investigators, Mollenhauer, B., & Schlossmacher, M. G. (2025). Development of a simplified smell test to identify Parkinson’s disease using multiple cohorts, machine learning and item response theory. npj Parkinson's Disease, 11(1), Article 85. https://doi.org/10.1038/s41531-025-00904-5 Marek, K., Chowdhury, S., Siderowf, A., Lasch, S., Coffey, C. S., Caspell‐Garcia, C., Simuni, T., Jennings, D., Tanner, C. M., Trojanowski, J. Q., Shaw, L. M., Seibyl, J., Schuff, N., Singleton, A., Kieburtz, K., Toga, A. W., Mollenhauer, B., Galasko, D., Chahine, L. M., . . . the Parkinson's Progression Markers Initiative. (2018). The Parkinson's progression markers initiative (PPMI) – establishing a PD biomarker cohort. Annals of Clinical and Translational Neurology, 5(12), 1460–1477. https://doi.org/10.1002/acn3.644 Markovic-Obiago, Z., Bestwick, J. P., Chohan, H., Schrag, A., Simonet, C., & Noyce, A. J. (2025). Idiopathic hyposmia as a marker of prodromal Parkinson’s disease — a cohort study. Scientific Reports, 15(1), Article 39501. https://doi.org/10.1038/s41598-025-23293-4 Mitchell, E., Mattjie, C., Bestwick, J. P., Barros, R. C., Schuh, A. F., Simonet, C., & Noyce, A. J. (2025). Hyposmia in Parkinson’s disease; exploring selective odour loss. npj Parkinson's Disease, 11(1), Article 67. https://doi.org/10.1038/s41531-025-00922-3 Moons, K. G. M., Damen, J. A. A., Kaul, T., Hooft, L., Andaur Navarro, C., Dhiman, P., Beam, A. L., Van Calster, B., Celi, L. A., Denaxas, S., Denniston, A. K., Ghassemi, M., Heinze, G., Kengne, A. P., Maier-Hein, L., Liu, X., Logullo, P., McCradden, M. D., Liu, N., . . . van Smeden, M. (2025). PROBAST+AI: An updated quality, risk of bias, and applicability assessment tool for prediction models using regression or artificial intelligence methods. BMJ, 388, Article e082505. https://doi.org/10.1136/bmj-2024-082505 Ponsen, M. M., Stoffers, D., Booij, J., van Eck‐Smit, B. L. F., Wolters, E. C., & Berendse, H. W. (2004). Idiopathic hyposmia as a preclinical sign of Parkinson's disease. Annals of Neurology, 56(2), 173–181. https://doi.org/10.1002/ana.20160 Postuma, R. B., Berg, D., Stern, M., Poewe, W., Olanow, C. W., Oertel, W., Obeso, J., Marek, K., Litvan, I., Lang, A. E., Halliday, G., Goetz, C. G., Gasser, T., Dubois, B., Chan, P., Bloem, B. R., Adler, C. H., & Deuschl, G. (2015). MDS clinical diagnostic criteria for Parkinson's disease. Movement Disorders, 30(12), 1591–1601. https://doi.org/10.1002/mds.26424 Riley, R. D., Debray, T. P. A., Collins, G. S., Archer, L., Ensor, J., van Smeden, M., & Snell, K. I. E. (2021). Minimum sample size for external validation of a clinical prediction model with a binary outcome. Statistics in Medicine, 40(19), 4230–4251. https://doi.org/10.1002/sim.9025 Ross, G. W., Petrovitch, H., Abbott, R. D., Tanner, C. M., Popper, J., Masaki, K., Launer, L., & White, L. R. (2008). Association of olfactory dysfunction with risk for future Parkinson's disease. Annals of Neurology, 63(2), 167–173. https://doi.org/10.1002/ana.21291 Siderowf, A., Concha-Marambio, L., Lafontant, D.-E., Farris, C. M., Ma, Y., Urenia, P. A., Nguyen, H., Alcalay, R. N., Chahine, L. M., Foroud, T., Galasko, D., Kieburtz, K., Merchant, K., Mollenhauer, B., Poston, K. L., Seibyl, J., Simuni, T., Tanner, C. M., Weintraub, D., . . . Soto, C. (2023). Assessment of heterogeneity among participants in the Parkinson's Progression Markers Initiative cohort using α-synuclein seed amplification: A cross-sectional study. The Lancet Neurology, 22(5), 407–417. https://doi.org/10.1016/s1474-4422(23)00109-6 Vickers, A. J., & Elkin, E. B. (2006). Decision curve analysis: A novel method for evaluating prediction models. Medical Decision Making, 26(6), 565–574. https://doi.org/10.1177/0272989x06295361

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Zenodo (CERN European Organization for Nuclear Research)
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2026-09-16
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https://doi.org/10.5281/zenodo.22782191
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Olfactory and Sensory Function Studies
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preprint

Olfactory Assessment for Affordable Parkinson Evaluation: Specificity, Enrichment, and the Cost of Follow-Up

Feride Yaldiz, Yavuz Selim Sılay, Kadir Tamrak, Salih Yaldız et al.
Zenodo (CERN European Organization for Nuclear Research)
Olfactory and Sensory Function Studies
preprint

Olfactory Assessment for Affordable Parkinson Evaluation: Specificity, Enrichment, and the Cost of Follow-Up

Feride Yaldiz, Yavuz Selim Sılay, Kadir Tamrak, Salih Yaldız, NeuralCipherai, Hasan Randa, Ömer Ağyol
preprint en

Abstract

Abstract Olfactory assessment is attractive as a comparatively low-burden component of Parkinson evaluation, but an inexpensive first test does not guarantee an affordable or clinically informative pathway. Smell loss has multiple causes, and the value of an abnormal result depends on the population, threshold, and subsequent assessment. This article combines a targeted synthesis of established and recent primary evidence with a reproducible hypothetical analysis of three olfactory operating points. Among 10,000 people with a specified target-condition prevalence of 1%, a sensitivity-oriented operating point with 90% sensitivity and 60% specificity generates 4,050 positive results, of which 90 are true positives. A more restrictive operating point with 60% sensitivity and 90% specificity generates 1,050 positives, of which 60 are true positives. Positive predictive values are 2.22% and 5.71%, respectively; neither supports standalone diagnosis. At the intermediate operating point, 80% sensitivity and specificity generate 2,060 follow-up assessments. An illustrative provider cost of five resource units for olfaction and sixty for follow-up produces 173,600 total units, while patient payments depend separately on the financing arrangement. A specificity-mixture analysis shows that applying a 90% specificity estimate to a population containing substantial non-Parkinson olfactory impairment can underestimate follow-up demand by 1,188 assessments. Recent abbreviated-test research supports investigation of shorter assessments, while recent work comparing Parkinson-related and non-Parkinson hyposmia cautions against assuming disease-specific odor patterns. The practical requirement is a staged pathway with appropriate comparison groups, valid local norms, transparent follow-up costs, and an alternative clinical route for persistent concerns. The numerical scenarios are hypothetical and do not establish the performance or price of any commercial test or platform. Article details NeuralCipherai; Kadir Tamrak; Salih Yaldız; Feride Yaldız; Ömer Ağyol; Yavuz Selim Silay; Hasan Randa Targeted evidence synthesis with hypothetical olfactory pathway analysis Three operating points, explicit patient-payment scenarios and a specificity-mixture analysis distinguish an inexpensive first test from an affordable informative assessment pathway. Project: NeuralCipher. Bibliographic records are supplied in BibTeX and RIS formats for reference-management software, including Zotero. Reproducible calculations and figure data accompany the article. References Berg, D., Postuma, R. B., Adler, C. H., Bloem, B. R., Chan, P., Dubois, B., Gasser, T., Goetz, C. G., Halliday, G., Joseph, L., Lang, A. E., Liepelt‐Scarfone, I., Litvan, I., Marek, K., Obeso, J., Oertel, W., Olanow, C. W., Poewe, W., Stern, M., & Deuschl, G. (2015). MDS research criteria for prodromal Parkinson's disease. Movement Disorders, 30(12), 1600–1611. https://doi.org/10.1002/mds.26431 Collins, G. S., Moons, K. G. M., Dhiman, P., Riley, R. D., Beam, A. L., Van Calster, B., Ghassemi, M., Liu, X., Reitsma, J. B., van Smeden, M., Boulesteix, A.-L., Camaradou, J. C., Celi, L. A., Denaxas, S., Denniston, A. K., Glocker, B., Golub, R. M., Harvey, H., Heinze, G., . . . Logullo, P. (2024). TRIPOD+AI statement: Updated guidance for reporting clinical prediction models that use regression or machine learning methods. BMJ, 385, Article e078378. https://doi.org/10.1136/bmj-2023-078378 Doty, R. L., Shaman, P., & Dann, M. (1984). Development of the university of pennsylvania smell identification test: A standardized microencapsulated test of olfactory function. Physiology & Behavior, 32(3), 489–502. https://doi.org/10.1016/0031-9384(84)90269-5 Haehner, A., Hummel, T., Hummel, C., Sommer, U., Junghanns, S., & Reichmann, H. (2007). Olfactory loss may be a first sign of idiopathic Parkinson's disease. Movement Disorders, 22(6), 839–842. https://doi.org/10.1002/mds.21413 Heinzel, S., Berg, D., Gasser, T., Chen, H., Yao, C., Postuma, R. B., & the MDS Task Force on the Definition of Parkinson's Disease. (2019). Update of the MDS research criteria for prodromal Parkinson's disease. Movement Disorders, 34(10), 1464–1470. https://doi.org/10.1002/mds.27802 Hummel, T., Kobal, G., Gudziol, H., & Mackay-Sim, A. (2007). Normative data for the “sniffin’ sticks” including tests of odor identification, odor discrimination, and olfactory thresholds: An upgrade based on a group of more than 3,000 subjects. European Archives of Oto-Rhino-Laryngology, 264(3), 237–243. https://doi.org/10.1007/s00405-006-0173-0 Hummel, T., Sekinger, B., Wolf, S. R., Pauli, E., & Kobal, G. (1997). ‘sniffin’ sticks': Olfactory performance assessed by the combined testing of odour identification, odor discrimination and olfactory threshold. Chemical Senses, 22(1), 39–52. https://doi.org/10.1093/chemse/22.1.39 Jennings, D., Siderowf, A., Stern, M., Seibyl, J., Eberly, S., Oakes, D., Marek, K., & PARS Investigators. (2017). Conversion to Parkinson disease in the PARS hyposmic and dopamine transporter-deficit prodromal cohort. JAMA Neurology, 74(8), 933–940. https://doi.org/10.1001/jamaneurol.2017.0985 Lee, K. J., Tilling, K. M., Cornish, R. P., Little, R. J. A., Bell, M. L., Goetghebeur, E., Hogan, J. W., & Carpenter, J. R. (2021). Framework for the treatment and reporting of missing data in observational studies: The Treatment And Reporting of Missing data in Observational Studies framework. Journal of Clinical Epidemiology, 134, 79–88. https://doi.org/10.1016/j.jclinepi.2021.01.008 Li, J., Grimes, K., Saade, J., Tomlinson, J. J., Mestre, T. A., Schade, S., Weber, S., Dakna, M., Wicke, T., Lang, E., Trenkwalder, C., Salmaso, N., Frank, A., Ramsay, T., Manuel, D., aSCENT-PD Investigators, Mollenhauer, B., & Schlossmacher, M. G. (2025). Development of a simplified smell test to identify Parkinson’s disease using multiple cohorts, machine learning and item response theory. npj Parkinson's Disease, 11(1), Article 85. https://doi.org/10.1038/s41531-025-00904-5 Marek, K., Chowdhury, S., Siderowf, A., Lasch, S., Coffey, C. S., Caspell‐Garcia, C., Simuni, T., Jennings, D., Tanner, C. M., Trojanowski, J. Q., Shaw, L. M., Seibyl, J., Schuff, N., Singleton, A., Kieburtz, K., Toga, A. W., Mollenhauer, B., Galasko, D., Chahine, L. M., . . . the Parkinson's Progression Markers Initiative. (2018). The Parkinson's progression markers initiative (PPMI) – establishing a PD biomarker cohort. Annals of Clinical and Translational Neurology, 5(12), 1460–1477. https://doi.org/10.1002/acn3.644 Markovic-Obiago, Z., Bestwick, J. P., Chohan, H., Schrag, A., Simonet, C., & Noyce, A. J. (2025). Idiopathic hyposmia as a marker of prodromal Parkinson’s disease — a cohort study. Scientific Reports, 15(1), Article 39501. https://doi.org/10.1038/s41598-025-23293-4 Mitchell, E., Mattjie, C., Bestwick, J. P., Barros, R. C., Schuh, A. F., Simonet, C., & Noyce, A. J. (2025). Hyposmia in Parkinson’s disease; exploring selective odour loss. npj Parkinson's Disease, 11(1), Article 67. https://doi.org/10.1038/s41531-025-00922-3 Moons, K. G. M., Damen, J. A. A., Kaul, T., Hooft, L., Andaur Navarro, C., Dhiman, P., Beam, A. L., Van Calster, B., Celi, L. A., Denaxas, S., Denniston, A. K., Ghassemi, M., Heinze, G., Kengne, A. P., Maier-Hein, L., Liu, X., Logullo, P., McCradden, M. D., Liu, N., . . . van Smeden, M. (2025). PROBAST+AI: An updated quality, risk of bias, and applicability assessment tool for prediction models using regression or artificial intelligence methods. BMJ, 388, Article e082505. https://doi.org/10.1136/bmj-2024-082505 Ponsen, M. M., Stoffers, D., Booij, J., van Eck‐Smit, B. L. F., Wolters, E. C., & Berendse, H. W. (2004). Idiopathic hyposmia as a preclinical sign of Parkinson's disease. Annals of Neurology, 56(2), 173–181. https://doi.org/10.1002/ana.20160 Postuma, R. B., Berg, D., Stern, M., Poewe, W., Olanow, C. W., Oertel, W., Obeso, J., Marek, K., Litvan, I., Lang, A. E., Halliday, G., Goetz, C. G., Gasser, T., Dubois, B., Chan, P., Bloem, B. R., Adler, C. H., & Deuschl, G. (2015). MDS clinical diagnostic criteria for Parkinson's disease. Movement Disorders, 30(12), 1591–1601. https://doi.org/10.1002/mds.26424 Riley, R. D., Debray, T. P. A., Collins, G. S., Archer, L., Ensor, J., van Smeden, M., & Snell, K. I. E. (2021). Minimum sample size for external validation of a clinical prediction model with a binary outcome. Statistics in Medicine, 40(19), 4230–4251. https://doi.org/10.1002/sim.9025 Ross, G. W., Petrovitch, H., Abbott, R. D., Tanner, C. M., Popper, J., Masaki, K., Launer, L., & White, L. R. (2008). Association of olfactory dysfunction with risk for future Parkinson's disease. Annals of Neurology, 63(2), 167–173. https://doi.org/10.1002/ana.21291 Siderowf, A., Concha-Marambio, L., Lafontant, D.-E., Farris, C. M., Ma, Y., Urenia, P. A., Nguyen, H., Alcalay, R. N., Chahine, L. M., Foroud, T., Galasko, D., Kieburtz, K., Merchant, K., Mollenhauer, B., Poston, K. L., Seibyl, J., Simuni, T., Tanner, C. M., Weintraub, D., . . . Soto, C. (2023). Assessment of heterogeneity among participants in the Parkinson's Progression Markers Initiative cohort using α-synuclein seed amplification: A cross-sectional study. The Lancet Neurology, 22(5), 407–417. https://doi.org/10.1016/s1474-4422(23)00109-6 Vickers, A. J., & Elkin, E. B. (2006). Decision curve analysis: A novel method for evaluating prediction models. Medical Decision Making, 26(6), 565–574. https://doi.org/10.1177/0272989x06295361

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