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Record W2811289559 · doi:10.21037/jss.2018.05.29

Evaluating cervical deformity corrective surgery outcomes at 1-year using current patient-derived and functional measures: are they adequate?

2018· article· en· W2811289559 on OpenAlexaff
Peter G. Passias, Samantha R. Horn, Cheongeun Oh, Subaraman Ramchandran, Douglas C. Burton, Virginie Lafage, Renaud Lafage, Gregory W. Poorman, Leah Steinmetz, Frank A. Segreto, Cole Bortz, Justin S. Smith, Christopher P. Ames, Christopher I. Shaffrey, Han Jo Kim, Alexandra Sorocéanu, Eric O. Klineberg

Bibliographic record

VenueJournal of Spine Surgery · 2018
Typearticle
Languageen
FieldMedicine
TopicScoliosis diagnosis and treatment
Canadian institutionsUniversity of Calgary
Fundersnot available
KeywordsMedicineDeformityCurrent (fluid)SurgeryEngineering

Abstract

fetched live from OpenAlex

BACKGROUND: Current health-related quality of life (HRQL) metrics used to assess patient outcomes following surgical correction of cervical deformity (CD) are not deformity-specific and thus cannot capture all aspects of a patient's deformity and outcomes. The purpose of this study is to evaluate the sensitivity of different HRQL outcome measures in assessing CD patients' outcomes 1-year post-operatively. METHODS: Retrospective review of prospective multi-center database. Inclusion criteria: CD patients ≥18 yrs with pre- and 1-year post-operative radiographs and HRQLs [modified Japanese Orthopaedic Association (mJOA), EuroQol five-dimensions (EQ-5D), neck disability index (NDI)]. Associations between changes in EQ5D and NDI with improvement at 1-year in mJOA scores were assessed by whether or not the patient met the minimum clinically important difference (MCID) as well as whether or not they improved by one or more categories (i.e., change from moderate to mild). Odds ratios reported with 95% confidence intervals. RESULTS: Sixty-three CD patients were included (mean 62 y, 55.6% F). Average baseline NDI scores were 46.75, mJOA was 13.68, and EQ-5D 0.74. Overall baseline myelopathy breakdown: none-9.5%, mild-30.2%, moderate-42.9%, high-17.5%. At 1-year, 46% of patients improved in mJOA, 71.4% NDI, and 65.1% EQ-5D. 19% of patients met mJOA MCID, 44.4% NDI MCID, 19% EQ-5D MCID. One-point improvement in NDI increased the odds of mJOA improvement and reaching mJOA MCID (improvement: OR, 1.06, CI: 1.01-1.10, P=0.01; MCID: OR, 1.06, CI: 1.02-1.11, P=0.006). Improvement in EQ-5D by 0.1 increased the odds of improving in mJOA and reaching mJOA MCID at 1-year (improvement: OR, 3.85, CI: 1.51-9.76, P=0.005; MCID: OR, 3.88, CI: 1.52-9.88, P=0.005). While correlations exist between outcome measures, when modeling these outcomes while controlling for confounders including cSVA change, surgical invasiveness, age and CCI, these HRQLs were not strongly correlated. CONCLUSIONS: Improvements in functional outcomes, as defined by mJOA score, were correlated with changes in neck based disability and general health state, defined by NDI and EQ-5D respectively. In an adjusted model, however, these direct relationships were not maintained. A CD-specific HRQL might be more useful for surgeons in assessing patient outcomes using a single metric.

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)0.018
metaresearch head score (Gemma)0.046
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesnone
DomainCandidate signal: Reporting · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.982
Threshold uncertainty score0.096

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0180.046
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.002
Bibliometrics0.0030.003
Science and technology studies0.0000.001
Scholarly communication0.0020.002
Open science0.0010.001
Research integrity0.0010.001
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 head0.183
GPT teacher head0.369
Teacher spread0.186 · 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.

Study designObservational
DomainReporting
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

Citations31
Published2018
Admission routes1
Has abstractyes

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