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Record W4394766187 · doi:10.3171/2023.12.spine23481

Are insufficient corrections a major factor in distal junctional kyphosis? A simulated analysis of cervical deformity correction using in-construct measurements

2024· article· en· W4394766187 on OpenAlexaff
Fares Ani, Ethan Sissman, Dainn Woo, Alex Soroceanu, Gregory M. Mundis, Robert K. Eastlack, Justin S. Smith, D. Kojo Hamilton, Han Jo Kim, Alan H. Daniels, Eric O. Klineberg, Brian J. Neuman, Daniel M. Sciubba, Munish C. Gupta, Khaled M. Kebaish, Peter G. Passias, Robert A. Hart, Shay Bess, Christopher I. Shaffrey, Frank J. Schwab, Virginie Lafage, Christopher P. Ames, Themistocles S. Protopsaltis

Bibliographic record

VenueJournal of Neurosurgery Spine · 2024
Typearticle
Languageen
FieldMedicine
TopicScoliosis diagnosis and treatment
Canadian institutionsUniversity of Calgary
Fundersnot available
KeywordsKyphosisConstruct (python library)DeformityOrthodonticsSpinal deformityMedicineComputer scienceSurgeryComputer networkRadiography

Abstract

fetched live from OpenAlex

OBJECTIVE: The present study utilized recently developed in-construct measurements in simulations of cervical deformity surgery in order to assess undercorrection and predict distal junctional kyphosis (DJK). METHODS: A retrospective review of a database of operative cervical deformity patients was analyzed for severe DJK and mild DJK. C2-lower instrumented vertebra (LIV) sagittal angle (SA) was measured postoperatively, and the correction was simulated in the preoperative radiograph in order to match the C2-LIV by using the planning software. Linear regression analysis that used C2 pelvic angle (CPA) and pelvic tilt (PT) determined the simulated PT that matched the virtual CPA. Linear regression analysis was used to determine the C2-T1 SA, C2-T4 SA, and C2-T10 SA that corresponded to DJK of 20° and cervical sagittal vertical axis (cSVA) of 40 mm. RESULTS: Sixty-nine cervical deformity patients were included. Severe and mild DJK occurred in 11 (16%) and 22 (32%) patients, respectively; 3 (4%) required DJK revision. Simulated corrections demonstrated that severe and mild DJK patients had worse alignment compared to non-DJK patients in terms of cSVA (42.5 mm vs 33.0 mm vs 23.4 mm, p < 0.001) and C2-LIV SVA (68.9 mm vs 57.3 mm vs 36.8 mm, p < 0.001). Linear regression revealed the relationships between in-construct measures (C2-T1 SA, C2-T4 SA, and C2-T10 SA), cSVA, and change in DJK (all R > 0.57, p < 0.001). A cSVA of 40 mm corresponded to C2-T4 SA of 10.4° and C2-T10 SA of 28.0°. A DJK angle change of 10° corresponded to C2-T4 SA of 5.8° and C2-T10 SA of 20.1°. CONCLUSIONS: Simulated cervical deformity corrections demonstrated that severe DJK patients have insufficient corrections compared to patients without DJK. In-construct measures assess sagittal alignment within the fusion separate from DJK and subjacent compensation. They can be useful as intraoperative tools to gauge the adequacy of cervical deformity correction.

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)0.007
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.002
Threshold uncertainty score0.005

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.007
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.000
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 head0.074
GPT teacher head0.333
Teacher spread0.259 · 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 designSimulation or modeling
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

Citations4
Published2024
Admission routes1
Has abstractyes

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