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Most previous outcome prediction models are classifiers. This constrains nuances in prediction and use for decision support.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Objectives<\/jats:title>\n                    <jats:p>To develop and test models for the prediction of continuous outcome scores and retrieval of similar patients\u2019 outcomes, and to evaluate the models\u2019 fairness.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Setting<\/jats:title>\n                    <jats:p>Norwegian public and private specialist healthcare.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Participants and data source<\/jats:title>\n                    <jats:p>All cases recorded with an elective operation for lumbar disc herniation (LDH, n=18\u2009377) or lumbar spinal stenosis (LSS, n=24\u2009540) in the Norwegian Registry for Spine Surgery from 1 January 2007 to 23 May 2023.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Outcome measures<\/jats:title>\n                    <jats:p>All outcomes were patient-reported 12 months after the operation. The primary outcome was the Oswestry disability index (ODI), modelled on a scale ranging from 0 to 100. Numeric Rating Scale scores (range 0\u201310)\u2009for back and leg pain were secondary outcomes.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Model building and performance<\/jats:title>\n                    <jats:p>\n                      We selected 22 predictors recorded preoperatively by patients and clinicians based on Shapley Additive Explanations values. Data were split into 80%\/20% training\/test samples for LDH and LSS. Six machine learning methods for regression, that is, with a continuous outcome (extreme gradient boosting (XGBoost), Gaussian process regression, gradient boosting regression, artificial neural networks and linear regression), were trained for both conditions using fivefold cross-validation. We report the magnitude and distribution of errors as mean absolute error (MAE) with 95% CIs, and explanatory power as the coefficient of determination (R\n                      <jats:sup>2<\/jats:sup>\n                      ). Fairness and calibration were assessed with violin and calibration plots of error. We developed a patient-similarity function that uses a K-nearest neighbour model to retrieve the individual outcomes of the 50 most similar patients and evaluated it by calculating L1 distances (Manhattan distances) across subgroups.\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>\n                      XGBoost regression performed best for both conditions. The models showed good calibration and predicted ODI with MAE 11.32 (95% CI 11.00 to 11.63) and R\n                      <jats:sup>2<\/jats:sup>\n                      0.27 (95% CI 0.24 to 0.29) for LDH and MAE 12.05 (95% CI 11.76 to 12.32) and R\n                      <jats:sup>2<\/jats:sup>\n                      0.31 (95% CI 0.28 to 0.34) for LSS. The MAEs for back and leg pain were 2.09 (95% CI 2.04 to 2.15) and 1.95 (95% CI 1.90 to 2.00) for LDH and 2.33 (95% CI 2.28 to 2.38) and 2.13 (95% CI 2.08 to 2.16) for LSS. All models were fair with differences in error between subgroups for sex, age, education level and native language. In the patient-similarity function, distances at baseline were evenly distributed across subgroups.\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusions<\/jats:title>\n                    <jats:p>Our machine learning models predicted continuous outcomes with MAEs close to the SEs of measurements. The models were fair across sociodemographic subgroups. We succeeded in developing a patient-similarity function which supplements the predictions.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1136\/bmjopen-2025-108947","type":"journal-article","created":{"date-parts":[[2026,5,13]],"date-time":"2026-05-13T14:25:45Z","timestamp":1778682345000},"page":"e108947","update-policy":"https:\/\/doi.org\/10.1136\/crossmarkpolicy","source":"Crossref","is-referenced-by-count":1,"title":["Prediction of Oswestry Disability Index and Numeric Rating Scale scores after lumbar spine surgery: machine learning model development and fairness assessment"],"prefix":"10.1136","volume":"16","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-0031-4961","authenticated-orcid":false,"given":"Henrik Lykke","family":"Joakimsen","sequence":"first","affiliation":[{"name":"Department of Clinical Medicine, UiT The Arctic University of Norway, Troms\u00f8, Troms, Norway"},{"name":"The Norwegian Registry for Spine Surgery and the Department of Neurosurgery, University Hospital of North Norway, Troms\u00f8, Troms, Norway"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7928-8435","authenticated-orcid":false,"given":"J\u00f8rgen Aarmo","family":"Lund","sequence":"additional","affiliation":[{"name":"Machine Learning Group, Faculty of Science and Technology, UiT The Arctic University of Norway, Troms\u00f8, Troms, Norway"},{"name":"The Norwegian Centre for Clinical Artificial Intelligence, University Hospital of North Norway, Troms\u00f8, Troms, Norway"},{"name":"DIPS AS, Bod\u00f8, Nordland, Norway"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0003-5072-3610","authenticated-orcid":false,"given":"Joel","family":"Burman","sequence":"additional","affiliation":[{"name":"The Norwegian Centre for Clinical Artificial Intelligence, University Hospital of North Norway, Troms\u00f8, Troms, Norway"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9464-3407","authenticated-orcid":false,"given":"Ashenafi Zebene","family":"Woldaregay","sequence":"additional","affiliation":[{"name":"Machine Learning Group, Faculty of Science and Technology, UiT The Arctic University of Norway, Troms\u00f8, Troms, Norway"},{"name":"The Norwegian Centre for Clinical Artificial Intelligence, University Hospital of North Norway, Troms\u00f8, Troms, Norway"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9017-5562","authenticated-orcid":false,"given":"Bj\u00f8rnar","family":"Berg","sequence":"additional","affiliation":[{"name":"Centre for Intelligent Musculoskeletal Health, Oslo Metropolitan University, Oslo, Oslo, Norway"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8991-8867","authenticated-orcid":false,"given":"Tore K","family":"Solberg","sequence":"additional","affiliation":[{"name":"Department of Clinical Medicine, UiT The Arctic University of Norway, Troms\u00f8, Troms, Norway"},{"name":"The Norwegian Registry for Spine Surgery and the Department of Neurosurgery, University Hospital of North Norway, Troms\u00f8, Troms, Norway"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5966-9786","authenticated-orcid":false,"given":"Tor","family":"Ingebrigtsen","sequence":"additional","affiliation":[{"name":"Department of Clinical Medicine, UiT The Arctic University of Norway, Troms\u00f8, Troms, Norway"},{"name":"The Norwegian Registry for Spine Surgery and the Department of Neurosurgery, University Hospital of North Norway, Troms\u00f8, Troms, Norway"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4672-7865","authenticated-orcid":false,"given":"Karl \u00d8yvind","family":"Mikalsen","sequence":"additional","affiliation":[{"name":"Machine Learning Group, Faculty of Science and Technology, UiT The Arctic University of Norway, Troms\u00f8, Troms, Norway"},{"name":"The Norwegian Centre for Clinical Artificial Intelligence, University Hospital of North Norway, Troms\u00f8, Troms, Norway"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"239","published-online":{"date-parts":[[2026,5,13]]},"reference":[{"key":"2026051307252336000_16.5.e108947.1","doi-asserted-by":"publisher","DOI":"10.1016\/S0140-6736(18)32279-7."},{"key":"2026051307252336000_16.5.e108947.2","doi-asserted-by":"crossref","unstructured":"Toll\u00e5nes MC , Knudsen AK , Vollset SE , et al . 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