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Record W4414560672 · doi:10.1097/corr.0000000000003691

Spine Stiffness Leads to High Pelvic Mobility: Uncoupling Native Mechanics and Explaining Why Patients With Stiff Spines Have Increased Dislocation Risk

2025· article· en· W4414560672 on OpenAlexaff
Jeroen Verhaegen, Moritz M. Innmann, Christian Merle, Nuno Alves Batista, Philippe Phan, George Grammatopoulos

Bibliographic record

VenueClinical Orthopaedics and Related Research · 2025
Typearticle
Languageen
FieldMedicine
TopicScoliosis diagnosis and treatment
Canadian institutionsOttawa Hospital
Fundersnot available
KeywordsSPINE (molecular biology)DislocationStiffnessBiomechanicsLumbar spineOrthopedic surgery

Abstract

fetched live from OpenAlex

BACKGROUND: Patients with stiff spines are at increased risk of instability after THA because of pelvic stiffness. Comprehensive study of patients with a stiff spine without hip arthritis could provide insight into native compensatory mechanisms and provide guidance on the mechanics to account for after arthroplasty. QUESTIONS/PURPOSES: The primary aim of this study was to characterize static and dynamic compensatory mechanics that occur in the presence of either a stiff hip or stiff spine. The secondary study aims were to assess which spinopelvic imaging modalities would best uncouple compensation mechanisms and to test the effect of length of spinal fusion (that is, number of fused segments) on the existing compensatory mechanics. METHODS: This was a prospective, case-control study performed at two academic tertiary referral centers. The cohort studied included three groups: (1) the control group of asymptomatic volunteers without signs of hip osteoarthritis or history of spinal surgery (n = 52); (2) the hip group of patients with osteoarthritis treated with THA between 2018 and 2019 (n = 512), excluding those with age < 18 years (n = 2), BMI > 40 kg/m 2 (n = 9), different diagnosis than osteoarthritis (n = 117), history of spinal or lower limb disease or surgery (n = 206), neurologic comorbidities (n = 17), absence of study consent (n = 20), or without spinopelvic radiographs (n = 17), in which the included patients (n = 124) were matched for age, sex, and BMI to the control group, resulting in the final hip group of 52 patients; and (3) the spine group were patients seen in clinic between 2023 and 2024 (n = 121), 1 year after spinal fusion, excluding those with BMI > 40 kg/m 2 (n = 10), hip osteoarthritis or surgery (n = 16), neuromuscular disease (n = 1), spinal fusion not including lumbar spine (n = 1), or without spinopelvic radiographs (n = 41), leaving 52 patients. The whole cohort comprised 60% (93 of 156) females, and the mean ± SD age was 64 ± 11 years. All underwent standing, relaxed-, and deep-seated radiographs to determine static characteristics: lumbar lordosis, pelvic tilt, pelvic-femoral angle, and pelvic incidence. Dynamic characteristics included difference in pelvic tilt, lumbar lordosis, and pelvic-femoral angles between standing and relaxed- or deep-seated positions, thereby determining which imaging modality best uncoupled compensatory mechanisms. Correlation between the number of fused segments and spinopelvic parameters was assessed using Spearman correlation coefficient. RESULTS: When standing, the spine group had a higher mean ± SD pelvic-femoral angle than the control (197° ± 7° versus 186° ± 10°, mean difference -11° [95% confidence interval (CI) -14° to -7°]; p < 0.001) and hip group (197° ± 7° versus 183° ± 11°, mean difference -14° [95% CI -18° to -10°]; p < 0.001) and a higher pelvic tilt compared with the control (20° ± 9° versus 15° ± 8°, mean difference -5° [95% CI -8° to -2°]; p = 0.003) and hip group (20° ± 9° versus 15° ± 7°, mean difference -5° [95% CI -9° to -2°]; p = 0.004). Dynamically, the spine group exhibited the least lumbar flexion (ΔLL) in both relaxed- (12° ± 11° versus 22° ± 12° versus 16° ± 12°; p = 0.002) and deep-seated transitions (25° ± 14° versus 43° ± 13° versus 43° ± 13°; p < 0.001). Between standing and deep-seated, change in pelvic tilt was greater in the spine group compared with the hip (20° ± 16° versus -6° ± 16°, mean difference -28° [95% CI -33° to -22°]; p < 0.001) and control group (20° ± 16° versus 4° ± 17°, mean difference -19° [95% CI -26° to -13°]; p < 0.001). Deep-seated, the spine group flexed the hip more than the hip group (109° ± 15° versus 70° ± 21°, mean difference -40° [95% CI -47° to -34°]; p < 0.001) and control group (109° ± 15° versus 85° ± 18°, mean difference -23° [95% CI -30° to -16°]; p < 0.001). Standing to deep-seated assessments better uncoupled compensatory mechanisms, as these detected differences between control and spine group (for instance, ∆LL standing/deep-seated 43° ± 13° versus 25° ± 14° [mean difference 19° (95% CI 14° to 25°); p < 0.001] versus ∆LL standing/relaxed-seated 16° ± 12° versus 12° ± 11° [mean difference 4° (95% CI 0° to 9°); p = 0.15]). The number of segments fused was associated with deep-seated lumbar lordosis (ρ = 0.55; p < 0.001) and pelvic tilt (ρ = -0.31; p = 0.02). CONCLUSION: In this study, patients with a stiff spine have hyperextended hips when standing and hyperflexed hips in a deep-seated position and exhibit a fivefold greater change in pelvic tilt between these positions compared with controls. The greater pelvic tilt change may cause an acetabular cup to be brought in a functionally suboptimal orientation, leading to impingement or dislocation. Deep-seated radiographs can uncouple compensatory mechanisms and are recommended to better identify patients with spinal stiffness. LEVEL OF EVIDENCE: Level II, diagnostic study.

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)0.001
metaresearch head score (Gemma)0.003
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 score0.004
Threshold uncertainty score0.013

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0040.000

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 head0.041
GPT teacher head0.388
Teacher spread0.347 · 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".

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Citations2
Published2025
Admission routes1
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