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Record W4406045704 · doi:10.3171/2024.8.spine24703

The gap between surgeon goal and achieved sagittal alignment in adult cervical spine deformity surgery

2025· article· en· W4406045704 on OpenAlexaff
Justin S. Smith, David Ben‐Israel, Michael P. Kelly, Virginie Lafage, Renaud Lafage, Eric O. Klineberg, Han Jo Kim, Breton Line, Themistocles S. Protopsaltis, Peter G. Passias, Robert K. Eastlack, Gregory M. Mundis, K. Daniel Riew, Khaled M. Kebaish, Paul Park, Munish C. Gupta, Jeffrey L. Gum, Alan H. Daniels, Bassel G. Diebo, Richard A. Hostin, Justin K. Scheer, Alex Soroceanu, D. Kojo Hamilton, Thomas J. Buell, Stephen J. Lewis, Lawrence G. Lenke, Jeffrey P. Mullin, Frank Schwab, Douglas C. Burton, Christopher I. Shaffrey, Christopher P. Ames, Shay Bess

Bibliographic record

VenueJournal of Neurosurgery Spine · 2025
Typearticle
Languageen
FieldMedicine
TopicScoliosis diagnosis and treatment
Canadian institutionsToronto Western HospitalUniversity of TorontoUniversity of Calgary
Fundersnot available
KeywordsSagittal planeMedicineSpinal deformityDeformityCobb angleSurgeryOrthodonticsScoliosisRadiology

Abstract

fetched live from OpenAlex

OBJECTIVE: Malalignment following cervical spine deformity (CSD) surgery can negatively impact outcomes and increase complications. Despite the growing ability to plan alignment, it remains unclear whether preoperative goals are achieved with surgery. The objective of this study was to assess how good surgeons are at achieving their preoperative goal alignment following CSD surgery. METHODS: Adult patients with CSD were prospectively enrolled into a multicenter registry. Surgeons documented alignment goals prior to surgery, including C2-7 sagittal vertical axis (SVA), C2-7 sagittal Cobb angle, T1 slope minus cervical lordosis (TS-CL), and C7-S1 SVA. Goals were compared with achieved alignment, and the offsets (achieved goal) were calculated. General linear models were created for offset magnitude for each alignment parameter, controlling for baseline deformity and surgical factors. RESULTS: The 88 enrolled patients had a mean age of 63.6 ± 13.0 years. The mean number of anterior and posterior instrumented levels was 3.5 ± 1.0 and 10.6 ± 4.5, respectively. Surgeons failed to achieve their preoperative alignment goals by an average of 17.2 (range 0.1-75.4) mm for C2-7 SVA, 10.3° (range 0.1°-45.5°) for C2-7 sagittal Cobb angle, 15.6° (range 0.0°-42.9°) for TS-CL, and 34.2 (range 0.3-113.7) mm for C7-S1 SVA. The sagittal alignment parameters with the highest rate of extreme outliers were TS-CL and C7-S1 SVA, with 32.2% exceeding 20° and 60.8% exceeding 20 mm from goal alignment, respectively. After controlling for baseline deformity and operative parameters, the only factor associated with achieving targeted alignment for C2-7 sagittal Cobb angle was greater baseline thoracic kyphosis (TK; B = -0.148, 95% CI -0.288 to -0.007, p = 0.040), and for TS-CL, the only associated factor was lower baseline TS-CL (B = 0.187, 95% CI 0.027-0.347, p = 0.022). Both lower TK and greater TS-CL may reflect increased baseline deformity through greater thoracic compensation and increased TS-CL mismatch, respectively. No significant associations were identified for C2-7 SVA and C7-S1 SVA. CONCLUSIONS: Surgeons failed to achieve their preoperative alignment goals by an average of 17.2 mm for C2-7 SVA, 10.3° for C2-7 sagittal Cobb angle, 15.6° for TS-CL, and 34.2 mm for C7-S1 SVA. The few factors identified that were associated with offset between goal and achieved alignment suggest that achievement of goal alignment was most challenging for more severe deformities. Further advancements are needed to enable more consistent translation of preoperative alignment goals into the operating room for adult CSD correction. Clinical trial registration no.: NCT01588054 (ClinicalTrials.gov).

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)0.003
metaresearch head score (Gemma)0.018
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 score0.003
Threshold uncertainty score0.017

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.018
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 science0.0000.001
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 head0.026
GPT teacher head0.292
Teacher spread0.266 · 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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