Evidence map›Paper›PMID 42256678›Full record

ArticleEClinicalMedicine2026

Generalisable prediction models for outcomes after lumbar spinal stenosis surgery: a model development and external validation study.

Bjørnar Berg, Allan Abbott, Casper Friis Pedersen, Henrik Hedevik, Martin A Gorosito, Henrik Lykke Joakimsen, Per Joel Burman, Karl Øyvind Mikalsen, Mikkel Østerheden Andersen, Tor Ingebrigtsen and 2 more

Abstract read
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Article in EClinicalMedicine, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

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Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

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No citing paper in PubMed yet.

4 · The record

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5 · Who and what money

Authors and funding

12 authors.

Bjørnar BergCentre for Intelligent Musculoskeletal Health, Faculty of Health Sciences, Oslo Metropolitan University, Oslo, Norway.
Allan AbbottUnit of Physiotherapy, Division of Prevention, Rehabilitation and Community Medicine, Department of Health, Medicine and Caring Sciences, Linköping, Sweden.
Casper Friis PedersenCenter for Spine Surgery and Research, Spine Center of Southern Denmark, Lillebaelt Hospital, University of Southern Denmark, Kolding, Denmark.
Henrik HedevikUnit of Physiotherapy, Division of Prevention, Rehabilitation and Community Medicine, Department of Health, Medicine and Caring Sciences, Linköping, Sweden.
Martin A GorositoCentre for Intelligent Musculoskeletal Health, Faculty of Health Sciences, Oslo Metropolitan University, Oslo, Norway.
Henrik Lykke JoakimsenDepartment of Clinical Medicine, Faculty of Health Sciences, UiT The Arctic University of Norway, Tromsø, Norway.
Per Joel BurmanThe Norwegian Centre for Clinical Artificial Intelligence, University Hospital of North Norway, Tromsø, Norway.
Karl Øyvind MikalsenDepartment of Clinical Medicine, Faculty of Health Sciences, UiT The Arctic University of Norway, Tromsø, Norway.
Mikkel Østerheden AndersenCenter for Spine Surgery and Research, Spine Center of Southern Denmark, Lillebaelt Hospital, University of Southern Denmark, Kolding, Denmark.
Tor IngebrigtsenDepartment of Clinical Medicine, Faculty of Health Sciences, UiT The Arctic University of Norway, Tromsø, Norway.
Tore SolbergDepartment of Clinical Medicine, Faculty of Health Sciences, UiT The Arctic University of Norway, Tromsø, Norway.
Margreth GrotleCentre for Intelligent Musculoskeletal Health, Faculty of Health Sciences, Oslo Metropolitan University, Oslo, Norway.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Background: Lumbar spinal stenosis is a leading indication for spine surgery, but outcomes are heterogeneous. We aimed to develop and externally validate prediction models for 12-month disability and pain to inform shared decision-making. Methods: This registry-based multicentre cohort study used data from three national spine registries of patients (≥16 years) undergoing elective lumbar spinal stenosis surgery. Data from the Norwegian Registry for Spine Surgery (NORspine, 2007-2023) were used for model development and internal-external cross-validation (IECV). External validation was carried out in the Swedish Registry (SweSpine, 2016-2022) and Danish Registry (DaneSpine, 2009-2022) with data collected by the Spine Centre of Southern Denmark. The primary outcome was the Oswestry Disability Index (ODI) at 12 months, modelled as a continuous and binary measure (acceptable symptom state). Secondary outcomes were Numeric Rating Scale (NRS) back and leg pain at 12 months. Logistic regression, linear regression, and XGBoost models were applied with 16 predictors. Missing data were handled using multiple imputation. Performance was assessed by calibration, mean absolute error (MAE), adjusted R Findings: The development cohort included 31,908 patients (52.4% female, 47.6% male). The external validation cohorts included 30,700 from SweSpine (52.8% female, 47.2% male) and 4063 from DaneSpine (54.6% female, 45.4% male). Twelve-month outcome completeness was 77% in the development cohort and ranged from 66% to 80% across the external validation cohorts. For ODI, linear regression achieved a pooled MAE of 12.4 (95% CI 11.8-13.1) after IECV, and 13.3 (95% CI 13.2-13.4) and 12.3 (95% CI 12.0-12.7) at external validation. Adjusted R Interpretation: Models predicting disability and pain were well calibrated and generalisable across Scandinavian countries, with the best overall performance for disability. These findings provide a foundation for prospective evaluation in future studies to determine the impact on decision-making and patient outcomes in clinical practice. Funding: Research Council of Norway.

Indexed as

External validationLumbar spinal stenosisMachine learningPrediction modelSpine surgery

Identifiers

PMID42256678
PMCPMC13233580

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.