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Record W4408634424 · doi:10.1097/brs.0000000000005336

Optimal Selection of Lower Instrumented Vertebra Can Minimize Distal Junctional Kyphosis After Posterior Spinal Fusion for Thoracic Adolescent Idiopathic Scoliosis

2025· article· en· W4408634424 on OpenAlexaff
Yusuke Hori, Akira Matsumura, Takashi Namikawa, Norihiro Isogai, Luiz Carlos Almeida da Silva, Burak Kaymaz, Petya Yorgova, Peter G. Gabos, Nicholas D. Fletcher, Michael P. Kelly, Harry L. Shufflebarger, Peter O. Newton, Burt Yaszay, Paul D. Sponseller, Baron S. Lonner, Amer F. Samdani, Firoz Miyanji

Bibliographic record

VenueSpine · 2025
Typearticle
Languageen
FieldMedicine
TopicScoliosis diagnosis and treatment
Canadian institutionsBC Children's Hospital
Fundersnot available
KeywordsMedicineKyphosisVertebraScoliosisReceiver operating characteristicSurgerySpinal fusionThoracic vertebraeSagittal planeLumbar vertebraeLumbarRadiologyRadiographyInternal medicine

Abstract

fetched live from OpenAlex

STUDY DESIGN: Retrospective cohort study of a prospectively collected multicenter database. OBJECTIVE: To identify risk factors for developing distal junctional kyphosis (DJK) and elucidate optimal selection of the lowest instrumented vertebra (LIV) utilizing sagittal stable vertebra (SSV) and preoperative distal junctional angle (DJA) to prevent DJK. SUMMARY OF BACKGROUND DATA: While including the SSV may minimize DJK following posterior spinal fusion (PSF) for adolescent idiopathic scoliosis, relying solely on the SSV criteria can necessitate more extensive fusion. As LIV moves distally, a patient's motion, function, and chance of degeneration may all be negatively affected. METHODS: This study included patients with Lenke 1/2 curves who underwent thoracic PSF (LIV≤L1); development of DJK (DJA≥10°) was evaluated 2 years postoperatively. Preoperative DJA was measured between LIV and LIV+1, consistent with postoperative measurements. Multiple logistic regression models identified risk factors for developing DJK. DeLong's test compared area under the curve (AUC) from different receiver operating characteristic curves to assess DJK predictive accuracy between models. RESULTS: Of 1,034 patients, 86 (8%) developed DJK 2 years postoperatively. Identified risk factors included preoperative DJA, LIV at ≥SSV-2, an upper instrumented vertebra of ≥T2, lumbar modifiers B or C, and larger T5-12 kyphosis. Incorporating preoperative DJA and SSV-1 for LIV selection enhanced DJK prediction accuracy over solely considering SSV inclusion (AUC=0.81 vs. 0.72, P<0.001). Furthermore, a multivariate model with risk factors achieved the highest AUC (0.87). Patients with DJK experienced worsening of T10-L2 kyphosis and lumbar lordosis over time, without affecting the Scoliosis Research Society-22 quality of life score. Among those who developed DJK, five required an extension of fixation distally. CONCLUSION: To prevent DJK, PSF should end below preoperative kyphosis and no more proximal than SSV-1 in patients with thoracic adolescent idiopathic scoliosis, particularly for high-risk cases. DJK led to kyphotic regional thoracolumbar alignment at 2-year follow-up. LEVEL OF EVIDENCE: Level Ⅲ-retrospective comparative 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)0.004
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 score0.002
Threshold uncertainty score0.005

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.013
GPT teacher head0.299
Teacher spread0.286 · 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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