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Record W2971593862 · doi:10.1097/brs.0000000000003237

Should Sagittal Spinal Alignment Targets for Adult Spinal Deformity Correction Depend on Pelvic Incidence and Age?

2019· article· en· W2971593862 on OpenAlexaff
Themistocles S. Protopsaltis, Alexandra Sorocéanu, Jared C. Tishelman, Aaron J. Buckland, Gregory M. Mundis, Justin S. Smith, Alan H. Daniels, Lawrence G. Lenke, Han Jo Kim, Eric O. Klineberg, Christopher P. Ames, Robert A. Hart, Shay Bess, Christopher I. Shaffrey, Frank J. Schwab, Virginie Lafage

Bibliographic record

VenueSpine · 2019
Typearticle
Languageen
FieldMedicine
TopicScoliosis diagnosis and treatment
Canadian institutionsUniversity of Calgary
Fundersnot available
KeywordsSpinal deformitySagittal planeMedicineIncidence (geometry)Pelvic tiltDeformityOrthodonticsPhysical medicine and rehabilitationAnatomySurgeryMathematicsGeometry

Abstract

fetched live from OpenAlex

STUDY DESIGN: Retrospective analysis. OBJECTIVE: Determine whether deformity corrections should vary by pelvic incidence (PI). SUMMARY OF BACKGROUND DATA: Alignment targets for deformity correction have been reported for various radiographic parameters. The T1 pelvic-angle (TPA) has gained in applications for adult spinal deformity (ASD) surgical-planning since it directly measures spinal alignment separate from pelvic- and lower-extremity compensation. Recent studies have demonstrated that ASD corrections should be age specific. METHODS: A prospective database of consecutive ASD patients was analyzed in conjunction with a normative spine database. Clinical measures of disability included the Oswestry Disability Index (ODI) and Short Form 36 Survey (SF-36) Physical Component Score (PCS). Baseline relationships between TPA, age, PI, and ODI/SF-36 PCS scores were analyzed in the ASD and asymptomatic patients. Linear regression modeling was used to determine alignment targets based on PI and age-specific normative SF-36-PCS values. RESULTS: Nine hundred three ASD patients (mean 53.7 yr) and 111 normative subjects (mean 50.7 yr) were included. Patients were subanalyzed by PI: low, medium, high (<40, 40-75, >75); and age: elderly (>65 yr, n = 375), middle age (45-65 yr, n = 387), and young (18-45 yr, n = 141). TPA and SRS-Schwab parameters correlated with age and PI in ASD and normative subjects (r = 0.42, P < 0.0001). ODI correlated with PCS (r = 0.71, P < 0.0001). Linear regression analysis using age-normative SF-36-PCS values demonstrated that ideal spinopelvic alignment is less strict with increasing PI and age. CONCLUSION: Targets for ASD correction should vary by age and PI. This is demonstrated in both asymptomatic and ASD subjects. Using age-normative SF-36 PCS values, alignment targets are described for different age and PI categories. High-PI patients do not require as rigorous realignments to attain age-specific normative levels of health status. As such, sagittal spinal alignment targets increase with increasing age as well as PI. LEVEL OF EVIDENCE: 3.

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)0.007
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 score0.016

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.007
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.001
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 head0.029
GPT teacher head0.321
Teacher spread0.292 · 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

Citations48
Published2019
Admission routes1
Has abstractyes

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