{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,30]],"date-time":"2026-06-30T19:00:14Z","timestamp":1782846014627,"version":"3.54.5"},"reference-count":22,"publisher":"Frontiers Media SA","license":[{"start":{"date-parts":[[2026,6,30]],"date-time":"2026-06-30T00:00:00Z","timestamp":1782777600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["frontiersin.org"],"crossmark-restriction":true},"short-container-title":["Front. Public Health"],"abstract":"<jats:sec>\n                    <jats:title>Background\/Objectives<\/jats:title>\n                    <jats:p>The COVID-19 pandemic exposed the need for economic resilience in dental practices, which faced reduced patient numbers, increased costs, and service restrictions. This study aimed to develop a predictive model for dental practice resilience and to identify the financial characteristics associated with better performance during and after a crisis.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>We analyzed official financial statements for 2,474 Bucharest dental practices. After applying mode-specific inclusion criteria, the final analytic cohorts comprised 850 firms for crisis-year prediction in 2020 and 801 firms for the three 2021 recovery scenarios. The Dental Practice Resilience Index (DPRI) was developed as a composite measure of profitability, expense efficiency, debt-to-turnover, turnover per employee, and profit per employee. Seven regression model families were evaluated across four prediction scenarios, and continuous predictions were also translated into tertile-based risk classes for decision support.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>\n                      Crisis-year prediction showed moderate but useful accuracy, with Linear Regression narrowly achieving the highest cross-validated performance (\n                      <jats:italic>R<\/jats:italic>\n                      <jats:sup>2<\/jats:sup>\n                      = 0.537 \u00b1 0.078). Predicting 2021 recovery from pre-crisis data alone was more difficult, and Random Forest narrowly ranked first (\n                      <jats:italic>R<\/jats:italic>\n                      <jats:sup>2<\/jats:sup>\n                      = 0.452 \u00b1 0.084). The strongest scenario-level results were obtained when observed 2020 data were included, with Elastic Net reaching\n                      <jats:italic>R<\/jats:italic>\n                      <jats:sup>2<\/jats:sup>\n                      = 0.643 \u00b1 0.071 and Random Forest reaching balanced accuracy = 0.648 \u00b1 0.044. When observed crisis-year data were unavailable, a two-step synthetic recovery approach recovered part of that information, but remained below the crisis-informed configuration.\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusions<\/jats:title>\n                    <jats:p>Machine learning methods proved effective not only in predicting dental practice resilience during a crisis, but also in anticipating post-crisis recovery potential. The DPRI provided an interpretable resilience measure, while the combination of continuous prediction and tertile-based classification offered both nuanced forecasts and practical risk stratification. Observed crisis-year information substantially improved recovery forecasting, although pre-crisis indicators alone still retained meaningful predictive value.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.3389\/fpubh.2026.1858605","type":"journal-article","created":{"date-parts":[[2026,6,30]],"date-time":"2026-06-30T18:02:56Z","timestamp":1782842576000},"update-policy":"https:\/\/doi.org\/10.3389\/crossmark-policy","source":"Crossref","is-referenced-by-count":0,"title":["Anticipating the economic resilience of dental practices in crises: a machine learning approach"],"prefix":"10.3389","volume":"14","author":[{"given":"Delia","family":"Radoi","sequence":"first","affiliation":[{"name":"Department of Organization, Professional Legislation and Dental Office Management, Faculty of Dentistry, Carol Davila University of Medicine and Pharmacy","place":["Bucharest, Romania"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Dan","family":"Curavale","sequence":"additional","affiliation":[{"name":"National University of Science and Technology POLITEHNICA Bucharest","place":["Bucharest, Romania"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Silviu-Mirel","family":"Pituru","sequence":"additional","affiliation":[{"name":"Department of Organization, Professional Legislation and Dental Office Management, Faculty of Dentistry, Carol Davila University of Medicine and Pharmacy","place":["Bucharest, Romania"]}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1965","published-online":{"date-parts":[[2026,6,30]]},"reference":[{"key":"B1","doi-asserted-by":"publisher","first-page":"63","DOI":"10.1016\/j.bar.2005.09.001","article-title":"35 years of studies on business failure: an overview of the classic statistical methodologies and their related 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