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Record W4409342402 · doi:10.2196/66889

Effect of a Digital Health Exercise Program on the Intention for Spinal Surgery in Adult Spinal Deformity: Exploratory Cross-Sectional Survey

2025· article· en· W4409342402 on OpenAlexvenueno aff
Marsalis Brown, Christopher Q. Lin, Christopher Jin, Matthew Rohde, Brett Rocos, Jonathan Belding, Barrett I. Woods, Stacey J. Ackerman

Bibliographic record

VenueJMIR Formative Research · 2025
Typearticle
Languageen
FieldMedicine
TopicScoliosis diagnosis and treatment
Canadian institutionsnot available
Fundersnot available
KeywordsPreprintSpinal deformityCross-sectional studyMedicineSpinal surgeryPhysical therapyExploratory researchPhysical medicine and rehabilitationDeformitySurgeryComputer science

Abstract

fetched live from OpenAlex

Background: Adult spinal deformity (ASD) is a prevalent condition estimated at 38%. Symptomatic ASD is associated with substantial health care costs. The role of nonoperative interventions in the management of ASD remains elusive. The National Scoliosis Clinic's (NSC) scoliosis realignment therapy (SRT) is a personalized digital health exercise program for the nonoperative management of ASD. Objective: This exploratory study had two objectives: (1) to evaluate the effect of the SRT program on users' intention of having spinal fusion; and (2) from a US payer perspective, to estimate the annual cost savings per 100,000 beneficiaries by averting spinal surgery. Methods: Individuals were enrolled in the SRT study from October 1, 2023 to September 1, 2024. Participants completed a web-based, cross-sectional survey about their history of prior scoliosis surgery and intent of having surgery before and after use of SRT (on a 4-point Likert scale, where 1 = "No Intent for Surgery" and 4 = "High Intent for Surgery"). Intent for surgery before and after participation in SRT was compared using a nonparametric Wilcoxon signed-rank test for paired data. Annual cost savings per 100,000 beneficiaries by averting spinal fusions were estimated separately for commercial payers and Medicare using published literature and public data sources. Payer expenditures were inflation-adjusted to 2024 US dollars using the Hospital Services component of the Consumer Price Index. Results: A total of 62 NSC members (38.8%) responded to the survey and were enrolled in the SRT program for an average (SD) of 17 (12) weeks. The mean (SD) age was 65.3 (13.5) years, and the majority were female (47/48, 98%) and White (45/46, 98%). Among the SRT users who did not have prior scoliosis surgery (n=56), 14% (8/56) reported a decrease in intent for surgery (that is, a lower Likert score) with the use of SRT. The mean (SD) intent for surgery scores before compared to after SRT were 1.29 (0.53) and 1.14 (0.35), respectively (mean difference 0.15 [P=.006]). Participants with "No Intent for Surgery" pre- versus postuse of SRT (42/56 versus 48/56, respectively) corresponded to an absolute risk reduction of 11% and a number needed to treat of 9 to avert one spinal fusion. Among the 6 participants who transitioned to "No Intent" for spinal surgery with the use of SRT, 3 were aged <65 years and 3 were ≥65 years of age. The annual cost savings from averted spinal surgeries were estimated at US $415,000 per 100,000 commercially-insured beneficiaries and US $617,000 per 100,000 Medicare beneficiaries. Conclusions: SRT is a personalized, scoliosis-specific digital health exercise program with the potential for averting 1 spinal surgery for every 9 participants, resulting in a substantial reduction in payer expenditures while improving the quality of care for commercial payers and Medicare beneficiaries.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.006
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.011
Threshold uncertainty score0.023

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.006
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.001

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.147
GPT teacher head0.506
Teacher spread0.359 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations2
Published2025
Admission routes1
Has abstractyes

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