{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,15]],"date-time":"2026-09-15T06:04:32Z","timestamp":1789452272523,"version":"build-2803163510"},"reference-count":19,"publisher":"Frontiers Media SA","license":[{"start":{"date-parts":[[2026,9,15]],"date-time":"2026-09-15T00:00:00Z","timestamp":1789430400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100004561","name":"Ministry of Education and Science of the Republic of Kazakhstan","doi-asserted-by":"publisher","id":[{"id":"10.13039\/501100004561","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["frontiersin.org"],"crossmark-restriction":true},"short-container-title":["Front. Rehabil. Sci."],"abstract":"<jats:sec>\n                    <jats:title>Objectives<\/jats:title>\n                    <jats:p>Work disability assessment is a key component of occupational health systems, influencing compensation, return-to-work decisions, and workforce management. However, variability in expert evaluations remains a major challenge. This study aims to identify recurring population-level patterns of disability severity using large-scale medical expert data.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>A retrospective dataset of 4,863 disability assessments conducted between 2020 and 2024 was analyzed. After applying inclusion criteria, 2,065 observations across 161 ICD-10 diagnostic categories were included. Statistical analysis involved empirical distribution modeling, concentration function estimation, confidence interval analysis, and hypothesis testing. Robustness was assessed using Monte Carlo resampling. Sensitivity analyses indicated that the predefined intervals were frequently reproduced as dominant concentration intervals under repeated resampling.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>\n                      Disability severity showed distinct concentration patterns across diagnostic groups. Moderate impairment cases were concentrated within the 30%\u201345% interval, while more severe cases clustered within the 46%\u201359% interval. These intervals captured more than 90% of observations in the major diagnostic groups. Differences in disability distributions between diagnostic groups were statistically significant (\n                      <jats:italic>p<\/jats:italic>\n                      \u2009&amp;lt;\u20090.001). Sensitivity analyses indicated that the identified interval boundaries were recurrent under repeated resampling.\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusions<\/jats:title>\n                    <jats:p>The proposed interval-based approach provides a reproducible, data-driven representation of work disability severity. Rather than demonstrating improved consistency in expert assessment, the findings identify recurring concentration patterns that may provide a quantitative reference for further research on disability assessment. Further external and clinical validation is required before the approach can be considered for implementation in disability assessment practice. These findings are relevant for population-level assessment of work disability and may inform public health monitoring and workforce participation policies.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.3389\/fresc.2026.1851082","type":"journal-article","created":{"date-parts":[[2026,9,15]],"date-time":"2026-09-15T05:35:42Z","timestamp":1789450542000},"update-policy":"https:\/\/doi.org\/10.3389\/crossmark-policy","source":"Crossref","is-referenced-by-count":0,"title":["Statistical interval-based assessment of work disability using large-scale medical data: implications for public health and workforce participation"],"prefix":"10.3389","volume":"7","author":[{"given":"Lyazat M.","family":"Aktayeva","sequence":"first","affiliation":[{"name":"Republican Research Institute for Occupational Safety and Health, Ministry of Labor and Social Protection of the Population of the Republic of Kazakhstan","place":["Astana, Kazakhstan"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Aiman S.","family":"Iskakova","sequence":"additional","affiliation":[{"name":"Republican Research Institute for Occupational Safety and Health, Ministry of Labor and Social Protection of the Population of the Republic of Kazakhstan","place":["Astana, Kazakhstan"]}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1965","published-online":{"date-parts":[[2026,9,15]]},"reference":[{"key":"B1","doi-asserted-by":"publisher","first-page":"893","DOI":"10.23736\/S1973-9087.24.07984-X","article-title":"The international classification of functioning, disability and health (ICF) core sets for deafblindness, part II of the systematic review: linking data to the ICF categories.","volume":"60","author":"Jaiswal","year":"2024","journal-title":"Eur J Phys Rehabil Med"},{"key":"B2","doi-asserted-by":"publisher","first-page":"241","DOI":"10.1007\/s10926-018-9788-4","article-title":"International classification 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